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Showing papers on "Feature (machine learning) published in 1992"


Journal ArticleDOI
TL;DR: A fundamental technique for designing a classifier that approaches the objective of minimum classification error in a more direct manner than traditional methods is given and is contrasted with several traditional classifier designs in typical experiments to demonstrate the superiority of the new learning formulation.
Abstract: A formulation is proposed for minimum-error classification, in which the misclassification probability is to be minimized based on a given set of training samples. A fundamental technique for designing a classifier that approaches the objective of minimum classification error in a more direct manner than traditional methods is given. The method is contrasted with several traditional classifier designs in typical experiments to demonstrate the superiority of the new learning formulation. The method can applied to other classifier structures as well. Experimental results pertaining to a speech recognition task are provided to show the effectiveness of the technique. >

759 citations


Proceedings ArticleDOI
23 Feb 1992
TL;DR: The effect of selecting varying numbers and kinds of features for use in predicting category membership was investigated on the Reuters and MUC-3 text categorization data sets and the optimal feature set size for word-based indexing was found to be surprisingly low despite the large training sets.
Abstract: The effect of selecting varying numbers and kinds of features for use in predicting category membership was investigated on the Reuters and MUC-3 text categorization data sets. Good categorization performance was achieved using a statistical classifier and a proportional assignment strategy. The optimal feature set size for word-based indexing was found to be surprisingly low (10 to 15 features) despite the large training sets. The extraction of new text features by syntactic analysis and feature clustering was investigated on the Reuters data set. Syntactic indexing phrases, clusters of these phrases, and clusters of words were all found to provide less effective representations than individual words.

585 citations


Journal ArticleDOI
TL;DR: Comparisons of textural features for pattern recognition show that co-occurrence features perform best followed by the fractal features, however, there is no universally best subset of features.

451 citations


Proceedings ArticleDOI
15 Jun 1992
TL;DR: A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented.
Abstract: A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model is biologically motivated, and the locations of the features often correspond to salient facial features such as the eyes, nose, etc. Topological graphs are used to represent relations between features, and a simple deterministic graph-matching scheme that exploits the basic structure is used to recognize familiar faces from a database. Each of the stages in the system can be fully implemented in parallel to achieve real-time recognition. Experimental results for a 128*128 image with very little noise are evaluated. >

361 citations


Journal ArticleDOI
TL;DR: This rule is used to show how Price's covariance equation is related to standard quantitative genetic results and to derive quantitative genetic equations for inclusive fitness and group selection and shows that the group-selection model is no more general than the inclusive-fitness viewpoint.
Abstract: Inclusive-fitness models have been criticized because they give incorrect results for cases in which fitness components interact nonadditively. However, this failure is not due to anything intrinsic to the inclusive-fitness viewpoint. It stems from an essentially quantitative genetic feature of the model, an attempt to separate fitness terms from genetic terms. A general rule is provided for determining when such a separation is justified. This rule is used to show how Price's covariance equation is related to standard quantitative genetic results and to derive quantitative genetic equations for inclusive fitness and group selection. It also shows that the group-selection model is no more general than the inclusive-fitness viewpoint. These models serve a role that is different from, but not inferior to, population-genetics models. Although they are less exact under some conditions, like quantitative genetic models in general, they provide us with measurable parameters.

305 citations


PatentDOI
TL;DR: In this paper, a speech coding and speech recognition apparatus is presented, where the value of at least one feature of an utterance is measured over each of a series of successive time intervals to produce the series of feature vector signals, and the closeness of the feature value of each feature vector signal to the parameter value of a set of prototype vector signals determined to obtain prototype match scores for each vector signal and each prototype vector signal.
Abstract: A speech coding and speech recognition apparatus. The value of at least one feature of an utterance is measured over each of a series of successive time intervals to produce a series of feature vector signals. The closeness of the feature value of each feature vector signal to the parameter value of each of a set of prototype vector signals is determined to obtain prototype match scores for each vector signal and each prototype vector signal. For each feature vector signal, first-rank and second-rank scores are associated with the prototype vector signals having the best and second best prototype match scores, respectively. For each feature vector signal, at least the identification value and the rank score of the first-ranked and second-ranked prototype vector signals are output as a coded utterance representation signal of the feature vector signal, to produce a series of coded utterance representation signals. For each of a plurality of speech units, a probabilistic model has a plurality of model outputs, and output probabilities for each model output. Each model output comprises the identification value of a prototype vector and a rank score. For each speech unit, a match score comprises an estimate of the probability that the probabilistic model of the speech unit would output a series of model outputs matching a reference series comprising the identification value and rank score of at least one prototype vector from each coded utterance representation signal in the series of coded utterance representation signals.

192 citations


Book
01 Nov 1992
TL;DR: This is the first book to offer a broad selection of state-of-the-art research papers, including authoritative critical surveys of the literature, and parallel studies of the architecture of complete high-performance printed-document reading systems.
Abstract: Document image analysis is the automatic computer interpretation of images of printed and handwritten documents, including text, drawings, maps, music scores, etc. Research in this field supports a rapidly growing international industry. This is the first book to offer a broad selection of state-of-the-art research papers, including authoritative critical surveys of the literature, and parallel studies of the architectureof complete high-performance printed-document reading systems. A unique feature is the extended section on music notation, an ideal vehicle for international sharing of basic research. Also, the collection includes important new work on line drawings, handwriting, character and symbol recognition, and basic methodological issues. The IAPR 1990 Workshop on Syntactic and Structural Pattern Recognition is summarized,including the reports of its expert working groups, whose debates provide a fascinating perspective on the field. The book is an excellent text for a first-year graduate seminar in document image analysis,and is likely to remain a standard reference in the field for years.

185 citations


Journal ArticleDOI
TL;DR: A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed and was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin.
Abstract: In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition. >

178 citations


PatentDOI
TL;DR: A speech coding apparatus compares the closeness of the feature value of a featurevector signal of an utterance to the parameter values of prototype vector signals to obtain prototype match scores for the feature vector signal and each prototype vector signal.
Abstract: A speech coding apparatus compares the closeness of the feature value of a feature vector signal of an utterance to the parameter values of prototype vector signals to obtain prototype match scores for the feature vector signal and each prototype vector signal. The speech coding apparatus stores a plurality of speech transition models representing speech transitions. At least one speech transition is represented by a plurality of different models. Each speech transition model has a plurality of model outputs, each comprising a prototype match score for a prototype vector signal. Each model output has an output probability. A model match score for a first feature vector signal and each speech transition model comprises the output probability for at least one prototype match score for the first feature vector signal and a prototype vector signal. A speech transition match score for the first feature vector signal and each speech transition comprises the best model match score for the first feature vector signal and all speech transition models representing the speech transition. The identification value of each speech transition and the speech transition match score for the first feature vector signal and each speech transition are output as a coded utterance representation signal of the first feature vector signal.

176 citations


PatentDOI
TL;DR: In this article, the similarity values of the symbol features of the observed events in the subsets are calculated, and the sets of contextual feature values associated with the events having the best "goodness of fit" are identified and form context-dependent bases for grouping observed events into two output sets.
Abstract: Symbol feature values and contextual feature values of each event in a training set of events are measured. At least two pairs of complementary subsets of observed events are selected. In each pair of complementary subsets of observed events, one subset has contextual features with values in a set Cn, and the other set has contextual features with values in a set Cn, were the sets in Cn and Cn are complementary sets of contextual feature values. For each subset of observed events, the similarity values of the symbol features of the observed events in the subsets are calculated. For each pair of complementary sets of observed events, a "goodness of fit" is the sum of the symbol feature value similarity of the subsets. The sets of contextual feature values associated with the subsets of observed events having the best "goodness of fit" are identified and form context-dependent bases for grouping the observed events into two output sets.

149 citations


PatentDOI
TL;DR: A speech recognition system includes a parameter extracting section for extracting a speech parameter of input speech, a first recognizing section for performing recognition processing by word-based matching, and a second recognizing sectionfor performing word recognition by matching in units of word constituent elements.
Abstract: A speech recognition system includes a parameter extracting section for extracting a speech parameter of input speech, a first recognizing section for performing recognition processing by word-based matching, and a second recognizing section for performing word recognition by matching in units of word constituent elements. The first word recognizing section segments the speech parameter in units of words to extract a word speech pattern and performs word recognition by matching the word speech pattern with a predetermined word reference pattern. The second word recognizing section performs recognition in units of word constituent elements by using the extracted speech parameter and performs word recognition on the basis of candidates of an obtained word constituent element series. The speech recognition system further includes a recognition result output section for obtaining a recognition result on the basis of the word recognition results obtained by the first and second recognizing sections and outputting the obtained recognition result. The speech recognition system further includes a word reference pattern learning section for performing learning of a word reference pattern on the basis of the recognition result obtained by the recognizing result output section and the word speech pattern.

Book ChapterDOI
19 May 1992
TL;DR: The first results of an ongoing project to compare several recognition strategies on a common database are presented.
Abstract: Several different techniques have been proposed for computer recognition of human faces. This paper presents the first results of an ongoing project to compare several recognition strategies on a common database.

Book
Yuichiro Anzai1
14 Jul 1992
TL;DR: An accelerated stochastic approximation algorithm is developed for the identification of weighting function associated with boundary control in one-dimensional linear distributed-parameter systems.
Abstract: An accelerated stochastic approximation algorithm is developed for the identification of weighting function associated with boundary control in one-dimensional linear distributed-parameter systems. The weighting function is assumed to be variable separable, and each variable is approximated by a finite number of orthonormal polynomials. In the absence of noise, this algorithm will converge in a finite number of steps. For adaptive control, on-line weighting function estimators are developed which use the optimal control function as input. These estimators are functional gradient algorithms based on least square approach. They can be used for estimating weighting function associated with either boundary or distributed control.

Journal ArticleDOI
TL;DR: An algorithm for recognition using neural-net-based techniques has been developed, and a suitable net architecture, which is similar to the multilayer perceptron in function and which implements the algorithm, has been designed.
Abstract: A new technique for performing form-feature recognition using the principles of neural networks is discussed. Neural nets require parallel input of data, which, in this case, are B-rep solid models of parts. An input format has been developed which includes face descriptions and face-face relationships. An algorithm for recognition using neural-net-based techniques has been developed, and a suitable net architecture, which is similar to the multilayer perceptron in function and which implements the algorithm, has been designed. The net architecture is described, and a few examples are presented which highlight the strengths and weaknesses of the recognition algorithm.

Proceedings ArticleDOI
30 Aug 1992
TL;DR: This paper proposes a face recognition method which is characterized by structural simplicity, trainability and high speed, and linearly combined on the basis of multivariate analysis methods to provide new effective features for face recognition in learning from examples.
Abstract: Proposes a face recognition method which is characterized by structural simplicity, trainability and high speed. The method consists of two stages of feature extractions: first, higher order local autocorrelation features which are shift-invariant and additive are extracted from an input image; then those features are linearly combined on the basis of multivariate analysis methods so as to provide new effective features for face recognition in learning from examples. >

Journal ArticleDOI
TL;DR: Recent priming experiments in which the effects of a prior brief presentation of an image on its subsequent recognition are assessed indicate that the invariance is complete: the magnitude of visual priming is not affected by a change in position, size, orientation in depth, or the particular lines and vertices present in the image, as long as representations of the same components can be activated.
Abstract: Phenomenologically, human shape recognition appears to be invariant with changes of orientation in depth (up to parts occlusion), position in the visual field, and size. Recent versions of template theories (e.g., Ullman, 1989; Lowe, 1987) assume that these invariances are achieved through the application of transformations such as rotation, translation, and scaling of the image so that it can be matched metrically to a stored template. Presumably, such transformations would require time for their execution. We describe recent priming experiments in which the effects of a prior brief presentation of an image on its subsequent recognition are assessed. The results of these experiments indicate that the invariance is complete: The magnitude of visual priming (as distinct from name or basic level concept priming) is not affected by a change in position, size, orienta- tion in depth, or the particular lines and vertices present in the image, as long as representations of the same components can be activated. An implemented seven layer neural network model (Hummel & Bicderman, 1992) that captures these fundamental properties of human object recognition is described. Given a line drawing of an object, the model activates a viewpoint-inva riant structural descrip- tion of the object, specifying its parts and their interrelations. Visual priming is interpreted as a change in the connection weights for the activation of: a) cells, termed geon feature assemblies (GFAs), that conjoin the output of units that represent invariant, independent properties of a single geon and its relations (such as its type, aspect ratio, relations to other geons), or b) a change in the connection weights by which several GFAs activate a cell representing an object.

Patent
04 Sep 1992
TL;DR: In this article, a pattern inspection apparatus for comparing/collating a test target pattern with a corresponding design pattern to detect the presence/absence of a defect which is present in the test target patterns includes a bit pattern generating circuit for developing the data of the design pattern into bits.
Abstract: A pattern inspection apparatus for comparing/collating a test target pattern with a corresponding design pattern to detect the presence/absence of a defect which is present in the test target pattern includes a bit pattern generating circuit for developing the data of the design pattern into bits, a corner pattern detector for scanning a corner pattern detection window having a predetermined range with respect to reference pattern data as a reference of a pattern obtained by bit development performed by the bit pattern generating circuit to extract a contour pattern, and detecting a corner pattern to be subjected to corner rounding processing on the basis of the extracted contour pattern, a memory for storing predetermined change information corresponding to the corner detected by the corner pattern detector, a graphic pattern synthesizing circuit for changing a graphic pattern in accordance with the information in the memory, and a comparing circuit for comparing reference pattern data, obtained by rounding processing performed on the basis of the reference pattern data and the feature of the corresponding pattern, with test pattern data obtained from the test target pattern, and further includes a pattern correcting circuit constituted by an excessive rounding detector for detecting and correcting an inadequate excessive rounding operation, and a pattern changing circuit for changing the pattern data in accordance with the excessive rounding detection result.

Proceedings Article
Subutai Ahmad1, Volker Tresp1
30 Nov 1992
TL;DR: It is shown how to obtain closed-form approximations to the Bayesian solution using Gaussian basis function networks and validated on a complex task (3D hand gesture recognition) to discuss Bayesian techniques for extracting class probabilities given partial data.
Abstract: In visual processing the ability to deal with missing and noisy information is crucial. Occlusions and unreliable feature detectors often lead to situations where little or no direct information about features is available. However the available information is usually sufficient to highly constrain the outputs. We discuss Bayesian techniques for extracting class probabilities given partial data. The optimal solution involves integrating over the missing dimensions weighted by the local probability densities. We show how to obtain closed-form approximations to the Bayesian solution using Gaussian basis function networks. The framework extends naturally to the case of noisy features. Simulations on a complex task (3D hand gesture recognition) validate the theory. When both integration and weighting by input densities are used, performance decreases gracefully with the number of missing or noisy features. Performance is substantially degraded if either step is omitted.

Journal ArticleDOI
TL;DR: A new methodology based on fuzzy-set-theoretic connectives to achieve information fusion in computer vision systems is introduced in this article, where the proposed scheme may be treated as a neural network in which fuzzy aggregation functions are used as activation functions.

Proceedings ArticleDOI
J. Franke1, E. Mandler1
30 Aug 1992
TL;DR: Two different approaches are described to combine the results of different classifiers based on the Dempster/Shafer theory of evidence and a statistical approach with some assumptions on the input data for user-dependent recognition of on-line handwritten characters.
Abstract: Two different approaches are described to combine the results of different classifiers. The first approach is based on the Dempster/Shafer theory of evidence and the second one is a statistical approach with some assumptions on the input data. Both approaches were tested for user-dependent recognition of on-line handwritten characters. >

Journal ArticleDOI
TL;DR: Results from a variety of other studies involving presentation of limited auditory signals, single-channel and multichannel implants, and tactual systems revealed a similar pattern among word recognition, overall consonant identification performance, and consonantal feature recruitment.
Abstract: A comprehensive set of speech reception measures were obtained in a group of about 20 postlingually deafened adult users of the Ineraid multichannel cochlear implant. The measures included audio, visual, and audiovisual recognition of words embedded in two types of sentences (with differing degrees of difficulty) and audio‐only recognition of isolated monosyllabic words, consonant identification (12 alternatives, /Ca/), and vowel identification (8 alternatives, /bVt/). For most implantees, the audiovisual gains in the sentence tests were very high. Quantitative relations among audio‐only scores were assessed using power‐law transformations suggested by Boothroyd and Nittrouer [J. Acoust. Soc. Am. 84, 101–114 (1988)] that can account for the benefit of sentence context (via a factor k) and the relation between word and phoneme recognition (via a factor j). Across the broad range of performance that existed among the subjects, substantial order was observed among measures of speech reception along the continuum from recognition of words in sentences, words in isolation, speech segments, and the retrieval of underlying phonetic features. Correlations exceeded 0.85 among direct and sentence‐derived measures of isolated word recognition as well as among direct and word‐derived measures of segmental recognition. Results from a variety of other studies involving presentation of limited auditory signals, single‐channel and multichannel implants, and tactual systems revealed a similar pattern among word recognition, overall consonant identification performance, and consonantal feature recruitment. Finally, improving the reception of consonantal place cues was identified as key to producing the greatest potential gains in speech reception.

Book ChapterDOI
Hermann Ney1
01 Jan 1992
TL;DR: A unifying framework of syntactic and statistical pattern recognition for one-dimensional observations and signals like speech is presented and it will be shown how these techniques can be applied to the task of continuous speech recognition.
Abstract: This paper presents a unifying framework of syntactic and statistical pattern recognition for one-dimensional observations and signals like speech. The syntactic constraints will be based upon stochastic extensions of the grammars in the Chomsky hierarchy. These extended stochastic grammars can be applied to both discrete and continuous observations. Neglecting the mathematical details and complications, we can convert a grammar of the Chomsky hierarchy to a stochastic grammar by attaching probabilities to the grammar rules and, for continuous observations, attaching probability density functions to the terminals of the grammar. In such a framework, a consistent integration of syntactic pattern recognition and statistical pattern recognition, which is typically based upon Bayes’ decision rule for minimum error rate, can be achieved such that no error correction or postprocessing after the recognition phase is required. Efficient algorithms and closed-form solutions for the parsing and recognition problem will be presented for the following types of stochastic grammars: regular, linear and context-free. It will be shown how these techniques can be applied to the task of continuous speech recognition.

Journal ArticleDOI
TL;DR: The role of and interaction between statistical, fuzzy, and neural-like models for certain problems associated with the three main areas of pattern recognition system design are discussed and some questions concerning fuzzy sets are answered.
Abstract: The role of and interaction between statistical, fuzzy, and neural-like models for certain problems associated with the three main areas of pattern recognition system design are discussed. Some questions concerning fuzzy sets are answered, and the design of fuzzy pattern recognition systems is reviewed. Pattern recognition, statistical pattern recognition and fuzzy pattern recognition systems are described. The use of computational neural-like networks in fuzzy pattern recognition is also discussed. >

01 Jan 1992
TL;DR: It is shown that recognition of gait can be achieved directly from motion features, without complex shape information, and that the motion information need not be finely quantized, which indicates the value of the structured connectionist paradigm in modeling perceptual processes.
Abstract: Recognition of motion sequences is a crucial ability for biological and robot vision systems. We present an architecture for the higher-level processes involved in recognition of complex structured motion. The work is focused on modeling human recognition of Moving Light Displays. MLDs are image sequences that contain only motion information at a small number of locations. Despite the extreme paucity of information in these displays, humans can recognize MLDs generated from a variety of common human movements. This dissertation explores the high-level representations and computational processes required for the recognition task. The structures and algorithms are articulated in the language of structured connectionist models. The implemented network can discriminate three human gaits from data generated by several actors. .pp Recognition of any motion involves indexing into stored models of movement. We present a representation for such models, called scenarios, based on coordinated sequences of discrete motion events. A method for indexing into this representation is described. We develop a parallel model of spatial and conceptual attention that is essential for disambiguating the spatially and temporally diffuse MLD data. The major computational problems addressed are: (1) representation of time-varying visual models; (2) integration of visual stimuli over time; (3) gestalt formation in and between spatially-localized feature maps and central movement representations; (4) contextual feedback to lower levels; and (5) the use of attention to focus processing on particular spatial locations and particular high-level representations. Several novel connectionist mechanisms are developed and used in the implementation. .pp In particular, we present advances in connectionist representation of temporal sequences and in using high-level knowledge to control an attentional mechanism. We show that recognition of gait can be achieved directly from motion features, without complex shape information, and that the motion information need not be finely quantized. We show how the "what" and "where" processes in vision can be tightly coupled in a synergistic fashion. These results indicate the value of the structured connectionist paradigm in modeling perceptual processes: no previous computational model has accounted for MLD recognition and we do not know how it would be approached in any other paradigm.

Proceedings ArticleDOI
A. Kawamura1, K. Yura1, T. Hayama1, Y. Hidai1, T. Minamikawa1, A. Tanaka, S. Masuda 
30 Aug 1992
TL;DR: The authors propose an online handwritten Japanese character recognition method permitting both stroke number and stroke order variations, based on the pattern matching technique, which has achieved a good recognition rate, 91%, for 2965 freely written Japanese kanji characters.
Abstract: The authors propose an online handwritten Japanese character recognition method permitting both stroke number and stroke order variations. The method is based on the pattern matching technique. Matching is done by the multiple similarity method using directional feature densities, which are independent of both stroke number and stroke order. This method has achieved a good recognition rate, 91%, for 2965 freely written Japanese kanji characters. >

Journal ArticleDOI
Z. Kuang, A. Kuh1
TL;DR: A neural network system which combines a self-organizing feature map and multilayer perception for the problem of isolated word speech recognition is presented, and an efficient adaptive nearby-search coding method based on the 'locality' of theSelf-organization is designed.
Abstract: A neural network system which combines a self-organizing feature map and multilayer perception for the problem of isolated word speech recognition is presented. A new method combining self-organization learning and K-means clustering is used for the training of the feature map, and an efficient adaptive nearby-search coding method based on the 'locality' of the self-organization is designed. The coding method is shown to save about 50% computation without degradation in recognition rate compared to full-search coding. Various experiments for different choices of parameters in the system were conducted on the TI 20 word database with best recognition rates as high as 99.5% for both speaker-dependent and multispeaker-dependent tests. >

Journal ArticleDOI
TL;DR: A linguistic recognition system based on approximate reasoning has been described which is capable of handling various imprecise input patterns and of providing a natural decision, thus providing a low rate of misclassification as compared to the conventional two-state system.

Proceedings ArticleDOI
01 Feb 1992
TL;DR: A novel recognition approach to human faces is proposed, which is based on the statistical model in the optimal discriminant space, which has very good recognition performance and recognition accuracies of 100 percent.
Abstract: Automatic recognition of human faces is a frontier topic in computer vision. In this paper, a novel recognition approach to human faces is proposed, which is based on the statistical model in the optimal discriminant space. Singular value vector has been proposed to represent algebraic features of images. This kind of feature vector has some important properties of algebraic and geometric invariance, and insensitiveness to noise. Because singular value vector is usually of high dimensionality, and recognition model based on these feature vectors belongs to the problem of small sample size, which has not been solved completely, dimensionality compression of singular value vector is very necessary. In our method, an optimal discriminant transformation is constructed to transform an original space of singular value vector into a new space in which its dimensionality is significantly lower than that in the original space. Finally, a recognition model is established in the new space. Experimental results show that our method has very good recognition performance, and recognition accuracies of 100 percent are obtained for all 64 facial images of 8 classes of human faces.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Book ChapterDOI
TL;DR: This article presented a model of bilingual word recognition which is derived from current unilingual dual route and verification models, and the model is able to account for an observed interlingual homophone effect, and successfully predicts the consequences which impaired functioning of this translator would have for a bilingual who is learning to read orthographically deep and shallow languages simultaneously.
Abstract: A model of bilingual word recognition is presented which is derived from current unilingual dual route and verification models. A core feature of this model is that word recognition involves obligatory grapheme-phoneme translation followed by an optional spelling check. The model is able to account for an observed interlingual homophone effect, and successfully predicts the consequences which impaired functioning of this translator would have for a bilingual who is learning to read orthographically deep and shallow languages simultaneously.

Journal ArticleDOI
01 Jul 1992
TL;DR: A recognition system based on fuzzy set theory and approximate reasoning that is capable of handling various imprecise input patterns and providing a natural decision system is described.
Abstract: A recognition system based on fuzzy set theory and approximate reasoning that is capable of handling various imprecise input patterns and providing a natural decision system is described. The input feature is considered to be of either quantitative form, linguistic form, mixed form, or set form. The entire feature space is decomposed into overlapping subdomains depending on the geometric structure and the relative position of the pattern classes found in the training samples. Uncertainty (ambiguity) in the input statement is managed by providing/modifying membership values to a great extent. A relational matrix corresponding to the subdomains and the pattern classes is used to recognize the samples. The system uses L.A. Zadeh's (1977) compositional rule of inference and gives a natural (linguistic) multivalued output decision associated with a confidence factor denoting the degree of certainty of a decision. The effectiveness of the algorithm is demonstrated for some artificially generated patterns and for real-life speech data. >