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Journal ArticleDOI

Artificial neural networks: a tutorial

01 Mar 1996-Vol. 29, Iss: 3, pp 31-44
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.
Abstract: Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model. It outlines network architectures and learning processes, and presents some of the most commonly used ANN models. It concludes with character recognition, a successful ANN application.
Citations
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Journal ArticleDOI
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

14,054 citations


Cites background from "Artificial neural networks: a tutor..."

  • ...(3) ANNs may learn their interconnec­tion weights adaptively [Jain and Mao 1996; Oja 1982]....

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  • ...Competitive (or winner take all) neural networks [Jain and Mao 1996] are often used to cluster input data....

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Journal ArticleDOI
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Abstract: The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

6,527 citations

Book ChapterDOI
Pavel Berkhin1
01 Jan 2006
TL;DR: This survey concentrates on clustering algorithms from a data mining perspective as a data modeling technique that provides for concise summaries of the data.
Abstract: Clustering is the division of data into groups of similar objects. In clustering, some details are disregarded in exchange for data simplification. Clustering can be viewed as a data modeling technique that provides for concise summaries of the data. Clustering is therefore related to many disciplines and plays an important role in a broad range of applications. The applications of clustering usually deal with large datasets and data with many attributes. Exploration of such data is a subject of data mining. This survey concentrates on clustering algorithms from a data mining perspective.

3,047 citations

Journal ArticleDOI
TL;DR: Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.

2,835 citations

Journal ArticleDOI
TL;DR: A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation ANNs theory and design, and a generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation is described.

2,622 citations


Cites background or methods from "Artificial neural networks: a tutor..."

  • ...…neuron generates an presented with an incomplete or noisy pattern, the output (or state) based on a Boltzmann statistical network responds by retrieving an internally stored distribution (Jain et al., 1996), which renders learnpattern that most closely resembles the presented ing extremely slower....

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  • ...Examples from this category are numerous in microbiology, e.g., predicting that the system will follow a certain trajectory based microbial growth (Geeraerd et al., 1998; Hajmeer et on system feedback (Jain et al., 1996). al., 1996, 1997, 1998)....

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  • ...…among the inputs and a layer consisting of output neurons. themselves such that only one neuron will be acti- The feedforward weights are used to select the vated in a given iteration with all the weights winning output neuron (cluster) and serve as the attached to it adjusted (Jain et al., 1996)....

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  • ...Jain et al. (1996) present a four-level classification based on applications may be solved using different ANN the degree of learning supervision, the learning rule, types, whereas others may only be solved via a data flow in the ANN, and the learning algorithm. specific ANN type....

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  • ...…cessing characteristics of the biological system such performing massively parallel computations for data as nonlinearity, high parallelism, robustness, fault and failure tolerance, learning, ability to handle imprecise and fuzzy information, and their capability to generalize (Jain et al., 1996)....

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References
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Journal ArticleDOI
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Abstract: Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.

16,652 citations

Journal ArticleDOI
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.

14,937 citations

Journal ArticleDOI
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
Abstract: The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.

9,775 citations

Book
01 Jan 1988

8,937 citations