Journal ArticleDOI
Optical Chinese character recognition using probabilistic neural networks
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TLDR
Some modifications to the standard approach implied by the probabilistic neural network structure are presented which yields significant speed improvements and are compared to using discriminant analysis and Geva and Sitte's Decision Surface Mapping classifiers.About:
This article is published in Pattern Recognition.The article was published on 1997-08-01. It has received 65 citations till now. The article focuses on the topics: Probabilistic neural network & Linear discriminant analysis.read more
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Patent
Keyboard system with automatic correction
TL;DR: In this article, an enhanced text entry system using word-level analysis to automatically correct inaccuracies in user keystroke entries on reduced keyboards was proposed, where the actual contact locations for the keystrokes may occur outside the boundaries of the specific keyboard key regions, and the distance from each contact location to each corresponding intended character may in general increase with the expected frequency of the intended word in the language or in a particular context.
Patent
Virtual keyboard system with automatic correction
Michael R. Longe,Pim Van Meurs +1 more
TL;DR: In this paper, an enhanced text entry system which uses word-level analysis to correct inaccuracies automatically in user keystroke entries on reduced-size or virtual keyboards is described, where the actual interaction locations for the keystrokes may occur outside the boundaries of the specific keyboard key regions associated with the actual characters of the word interpretations proposed or offered for selection.
Patent
Directional input system with automatic correction
TL;DR: A system associated with a text entry application, such as email or instant messaging, comprises an optional on-screen representation of a circular keyboard, a list of potential linguistic object matches, and a message area where the selected words are entered as mentioned in this paper.
Journal ArticleDOI
Probabilistic neural-network structure determination for pattern classification
Kezhi Mao,K.-C. Tan,Wee Ser +2 more
TL;DR: A supervised network structure determination algorithm that identifies an appropriate smoothing parameter using a genetic algorithm and determines suitable pattern layer neurons using a forward regression orthogonal algorithm is proposed.
Patent
Touch screen and graphical user interface
TL;DR: In this paper, a pointing device can be a touchpad, a mouse, a pen, or any device capable of providing two or three-dimensional location, and a representation of the location of the pointing device over a virtual keyboard/pad can be dynamically shown on an associated display.
References
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Journal ArticleDOI
Pattern Classification and Scene Analysis.
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Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Book
Self Organization And Associative Memory
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
Book
Classical and modern regression with applications
TL;DR: In this article, the authors focus on concepts with a blend between illustrations using real data sets and mathematical and conceptual development and emphasize applications with examples that illustrate nearly all the techniques discussed, including simultaneous influence, maximum likelihood estimation of parameters, and the plotting of residuals.