Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
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TLDR
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.Abstract:
Publisher Summary This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. 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. It closes with the discussion of performance and implementation issues.read more
Citations
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Reinforcement Learning: An Introduction
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A Tutorial on Support Vector Machines for Pattern Recognition
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A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
References
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Proceedings ArticleDOI
A new learning approach to enhance the storage capacity of the Hopfield model
Heekuck Oh,S.C. Kothari +1 more
TL;DR: A new learning technique is introduced to solve the problem of the small and restrictive storage capacity of the Hopfield model and exploits the maximum storage capacity.
Neural network processing as a tool for function optimization
W. Jeffrey,R. Rosner +1 more
TL;DR: In this article, the authors summarize the development of neural network-like processing for function optimization, and demonstrate how this method can be designed so as to avoid trapping in local extrema.
Book ChapterDOI
Neurocomputing formalisms for computational learning and machine intelligence
TL;DR: The chapter rederives a theoretical framework for neural learning of nonlinear mappings, wherein both the topology of the network and synaptic interconnection strengths are evolved adaptively, and exploits the concept of adjoint-operators to enable a fast global computation of a network's response to perturbations in all system parameters.
Book ChapterDOI
Collective computation, content-addressable memory, and optimization problems
TL;DR: A collective decision network is described which can function as a computational element in a digital computer or signal processor and differs from conventional digital circuit designs in emphasizing the large connectivity and analog response that biological “computational” systems employ.