By Gustavo Deco, Dragan Obradovic

ISBN-10: 1461240166

ISBN-13: 9781461240167

ISBN-10: 1461284694

ISBN-13: 9781461284697

Neural networks supply a strong new know-how to version and regulate nonlinear and intricate structures. during this ebook, the authors current a close formula of neural networks from the information-theoretic standpoint. They exhibit how this attitude offers new insights into the layout idea of neural networks. particularly they exhibit how those tools might be utilized to the themes of supervised and unsupervised studying together with characteristic extraction, linear and non-linear autonomous part research, and Boltzmann machines. Readers are assumed to have a easy realizing of neural networks, yet the entire suitable thoughts from info conception are rigorously brought and defined. hence, readers from a number of various medical disciplines, particularly cognitive scientists, engineers, physicists, statisticians, and machine scientists, will locate this to be a really helpful advent to this topic.

Show description

Read or Download An Information-Theoretic Approach to Neural Computing PDF

Similar intelligence & semantics books

Download PDF by Yiteng (Arden) Huang, Jacob Benesty: Audio Signal Processing for Next-Generation Multimedia

I'd say this ebook is a 5 megastar publication while you are a researcher in any of the subsequent parts: speech acquisition and enhancement, acoustic echo cancellation, sound resource monitoring and estimation, or audio coding and sound degree illustration. the maths is especially transparent for this kind of educational books written by way of committee with a few reliable examples.

Download e-book for iPad: Intelligent Systems and Technologies: Methods and by Lakhmi C. Jain, Chee Peng Lim (auth.), Horia-Nicolai

Clever structures and applied sciences are expanding discovering their methods in our day-by-day lives. This booklet offers a pattern of modern examine effects from key researchers. The contributions include:Introduction to clever systemsA Fuzzy Density research of Subgroups through DNA OligonucleotidesEvolution of Cooperating type ideas with an Archiving technique to Underpin CollaborationDesigning brokers with Dynamic CapabilityLocalized as opposed to Locality holding illustration equipment in Face popularity TasksInvariance houses of Recurrent Neural NetworksSolving Bioinformatics difficulties by means of smooth Computing TechniquesTransforming an Interactive specialist Code right into a Statefull carrier and a Multicoreenabled SystemRo-WordNet with Paradigmatic Morphology and Subjectivity Mark-upSpecial circumstances of Relative item Qualification utilizing the between OperatorEffective Speaker monitoring thoughts for Multi-party Human-Computer DialogueThe Fuzzy Interpolative keep watch over for Passive GreenhousesGPS security process for airplanes3D Collaborative Interfaces for E-learningOpen tasks in modern E-LearningSoftware Platform for Archaeological Patrimony stock and ManagementThe publication is directed to the graduate scholars, researchers, professors and the practitioner of clever platforms.

New PDF release: Intelligent Software Agents: Foundations and Applications

Ranging from case reviews, the authors boost a methodical illustration of clever software program brokers. After laying the principles they offer a finished description of the fundamental construction blocks of brokers and agent platforms. those contain particularly the architectural percentages for developing clever software program brokers and cooperation and verbal exchange in multiagent platforms.

Read e-book online Probability Collectives: A Distributed Multi-agent System PDF

This booklet presents an rising computational intelligence software within the framework of collective intelligence for modeling and controlling dispensed multi-agent structures known as likelihood Collectives. within the changed chance Collectives technique a couple of constraint dealing with ideas are included, which additionally reduces the computational complexity and more suitable the convergence and potency.

Additional info for An Information-Theoretic Approach to Neural Computing

Example text

E. ) with respect to the input ~ with the stochastic signal probability density function is p ( v) . Hence, the goal of the deterministic neural networh is to model the function I (~). 7) a = lk = 1 where P is the number of training patterns. Hence, the assumption of the additive Gaussian noise has led to the problem definition which is identical to the standard quadratic error minimization in the completely deterministic setting. Preliminaries of Information Theory and Neural Networks 30 The minimization of E can be performed by different optimization techniques.

Statistical independence implies that the probability distribution is factorizable. Decorrelation (diagonalization of the covariance matrix) and statistical independency are equivalent only in the Gaussian case (see Chapter 4 for more details about this fact). The next section presents an alternative derivation of PC A as the optimal linear compression method. 2 peA and Optimal Reconstruction This section focuses on reconstruction properties of PCA. 13) x where W is a M x N full-row-rank matrix and and y are the Nand M dimensional vectors respectively with N;Z M.

Let be the N-dimensional input vector distributed according to the probability density p (x) . The linear transformation from the N-dimensional input space to an M -dimensional Principal Component space is given by: x Principal Component Analysis: Statistical Approach 43 N Yj = L = 1, ... e. the input defined by: y = wi. Let Qx be the covariance matrix of • • (x-u) • .. 2) u= fp(i)di is the expectation operator and (i) is the mean value of the where ( . ) input vector. We snaIl assume for simplicity that = O.

Download PDF sample

An Information-Theoretic Approach to Neural Computing by Gustavo Deco, Dragan Obradovic

by David

New PDF release: An Information-Theoretic Approach to Neural Computing
Rated 4.16 of 5 – based on 38 votes