Revisão Bibliográfica: Learning Vector Quantization 28/04/2003
Referências adicionais para a teoria de LVQ
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Referências adicionais para a teoria de LVQ4.1. Livros 1) Haykin, Simon, “Neural Networks”, Macmillan Publishing Company, 1994. 2) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) - Bernhard Scholkopf, et al; Hardcover 3) Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) -- by Vojislav Kecman; Hardcover 4) An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini (Author), John Shawe-Taylor (Author) (Hardcover - March 2000) 5) Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) by Vojislav Kecman (Hardcover) 6) Advances in Kernel Methods: Support Vector Learning by Bernhard Scholkopf (Editor), et al (Hardcover) 7) Pattern Recognition With Support Vector Machines: First International Workshop, Svm 2002, Niagara Falls, Canada, August 10, 2002: Proceedings (Lecture Notes in Computer Science, 2388) by Alessandro Verri (Editor), et al (Paperback - November 2002) 8) Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms (Kluwer International Series in Engineering and Computer Science, 668) by Thorsten Joachims (Hardcover - April 2002) 4.2. Publicações em Congressos Internet: http://nips.djvuzone.org
4.3. Artigos 1. Luttrell, S.P., “Self Organization: A derivation from first principle of a class of learning algorithms.” IEEE Conference on Neural Networks, pp. 495-498, Washinton, DC, 1989. 3. T. Poggio and F. Girosi, "A Theory of Networks for Approximation and Learning," Technical Report 1140, MIT AI Lab, 1989. (67 citations) Abstract: Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multi-dimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. This paper considers the problems of an exact representation and, in more detail, of the approximation of linear and nonlinear mappings in terms of simpler functions of fewer variables. Kolmogorov's theorem concerning the representation of functions of several variables in terms of functions of one variable turns out to be almost irrelevant in the context of networks for learning. We develop a theoretical framework for approximation based on regularization techniques that leads to a class of three-layer networks that we call Generalized Radial Basis Functions. http://citeseer.nj.nec.com/poggio89theory.html http://citeseer.nj.nec.com/rd/74226632%2C530479%2C1%2C0.25%2CDownload/http://citeseer.nj.nec.com/cache/papers/cs/26152/ftp:zSzzSzpublications.ai.mit.eduzSzai-publicationszSzpdfzSzAIM-1140.pdf/poggio89theory.pdf 4. B. Fritzke. Let it grow - self-organizingfeature maps with problem dependent cell structure. In T. Kohonen et al., editor, Artificial Neural Networks; (ICANN-91), volume 1, pages 403--408, Amsterdam, The Netherlands, 1991. North-Holland; Elsevier Science Publishing Company B.V. (21 citations) Abstract: The self-organizing feature maps introduced by T. Kohonen use a cell Array of fixed size and structure. In many cases this array is not able to model a given signal distribution properly. We present a method to construct two-dimensional cell structures during a self-organization process which are specially adapted to the underlying distribution: Starting with a small number of cells new cells are added successively. Hereby signal vectors according to the (usually not explicitly known) probability distribution are used to determine where to insert or delete cells in the current structure. This process leads to problem dependent cell structures which model the given distribution with arbitrary high accuracy. http://citeseer.nj.nec.com/fritzke91let.html http://citeseer.nj.nec.com/rd/74226632%2C514611%2C1%2C0.25%2CDownload/http://citeseer.nj.nec.com/compress/0/papers/cs/13860/ftp:zSzzSzftp.cis.ohio-state.eduzSzpubzSzneuroprosezSzfritzke.cell_structures.ps.gz/fritzke91let.ps Orthogonal Matching Pursuit: Recursive Function.. - Pati, Rezaiifar.. (1993) Shadows in Linear Lattices - Weidl (1996) What is the nearest neighbor in high dimensional spaces? - Hinneburg, Aggarwal, Keim (2000) On Nonparametric Density Estimation In Sup-Norm - Korostelev (1996) Geometrical approach to parameter dependent Lyapunov functions - Ogata Yamamoto Liu Occurrences in Debugger Specifications - Bertot (1991) Eigenvalue Completions By Affine Varieties - Rosenthal, Wang (1997) Special Classes of Positive and Completely Positive Maps - Li, Woerdeman Coq in Coq - Bruno Barras (1997) 4.4. Tutoriais User's Guide - Neureka Ans The Learning vector quantization networks . ftp.ii.uib.no/pub/neureka/doc/nn.2.2.1/user.ps.gz Abstract: This document is the user's guide to nn, a specification language for modular, layered neural networks and to xnn, the graphical user interface the nn compiler and to networks generated by the compiler. We show how to use… Tutorial: Artificial Neural Networks for Document Analysis and ... - ... In this tutorial, we presents a survey of most significant tasks ... models like multiplayer perceptrons, radial basis functions, and learning vector quantization. ... www.dsi.unifi.it/~simone/ANNxDAR/ www.ibiblio.org/pub/academic/computer-science/neural-networks/papers/CNS_BBS_pointer - ... ZIP (DOS) ANN tutorial 3/4 DEANNA4.ZIP (DOS) ANN tutorial 4/4 ... patch, use to uncompress .lzh files LVQ_DOCS.LZH (UNIX) Learning Vector Quantization package v1 ... ICANN 98, Tutorial 3 - ... The main themes of this much updated tutorial are the following: Introduction to data exploration. ... Learning Vector Quantization and Batch-LVQ. ... www.ida.his.se/ida/icann98/tutorial3.html
... From this tutorial you will learn: How to use the essentials of classical vector quantization, the self-organizing maps and learning vector quantization in ... www.ewh.ieee.org/soc/ias/chapters-memb-dept/ techvideo.html NIPS: Neural Information Processing Systems, 1994 Tutorials - ... This tutorial contains the following topics: Introduction to neural computing and competitive learning ... Learning Vector Quantization (LVQ) and its applications. ... www.cs.cmu.edu/Groups/NIPS/1994/94tutorials.html Tech Tutorial - Introduction to Artificial Neural Networks - ... feature maps, learning vector quantization, radial basis function and Hopfield neural networks are provided. Applications that are covered in this tutorial are ... www.spie.org/web/meetings/programs/pe97/sc/tt05.html - 3k - Em cache - Páginas Semelhantes
algorithms for ... We then motivate mixture models and vector quantization. ... www.ii.uam.es/icann2002/ T5_Ghahramani_probabilistic.htm
... neural networks; Different neural network learning algorithms; ... coding is then given, followed by Vector Quantization. ... This tutorial enables the participant to: ... venus.ece.ndsu.nodak.edu/ece/research/ conferences/icimade01/tutorial.html
... [pdf], Learning Vector Quantization (LVQ). [pdf]. ... Please do not ask him for help outside the tutorial sessions - that is not his job! ... www.cs.bham.ac.uk/~jxb/inn.html COMP327: Tutorial six Formato do arquivo: PDF/Adobe Acrobat - Ver em HTML ... More examples of unsupervised learning * Finding unknown groups ?clustering ... vectors (usually continuous) by a discrete number ?vector quantization * It can ... www.cs.ust.hk/~martin/comp327/t6old.pdf
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