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Neural networks: a pattern recognition perspective
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This article shows how neural networks can be placed on a principled, statistical foundation, and discusses some of the practical benefits which this brings.Abstract:
The majority of current applications of neural networks are concerned with problems in pattern recognition. In this article we show how neural networks can be placed on a principled, statistical foundation, and we discuss some of the practical benefits which this brings.read more
Citations
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Journal ArticleDOI
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
TL;DR: An overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids is provided.
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Learning from data: a tutorial with emphasis on modern pattern recognition methods
M. Pardo,Giorgio Sberveglieri +1 more
TL;DR: Boosting is introduced, in which classifiers adaptively concentrate on the harder examples located near to the classification boundary and output coding, where a set of independent two-class machines solves a multiclass problem.
Journal ArticleDOI
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
TL;DR: In this paper, the authors provide an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids.
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Active Learning-Based Pedagogical Rule Extraction
TL;DR: The results show that not only do the generated rules explain the black-box models well (thereby facilitating the acceptance of such models), the proposed algorithm also performs significantly better than traditional rule induction techniques in terms of accuracy as well as fidelity.
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Discrimination and Classification.
B. A. Moore,David J. Hand +1 more
TL;DR: The authors presents different approaches to discrimination and classification problems from a statistical perspective and provides computer projects concentrating on the most widely used and important algorithms, numerical examples, and theoretical questions reinforce to further develop the ideas introduced in the text.
References
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Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Journal ArticleDOI
Approximation by superpositions of a sigmoidal function
TL;DR: It is demonstrated that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.