Results 1 - 10 of 5,770

Survey on Independent Component Analysis

by Aapo Hyvärinen - NEURAL COMPUTING SURVEYS , 1999
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
Abstract - Cited by 2309 (104 self) - Add to MetaCart
of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes

Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems

by Mário A. T. Figueiredo, Robert D. Nowak, Stephen J. Wright - IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING , 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
Abstract - Cited by 539 (17 self) - Add to MetaCart
sparseness-inducing (ℓ1) regularization term.Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution, and compressed sensing are a few well-known examples of this approach. This paper proposes gradient projection (GP) algorithms for the bound

Learning Decision Lists

by Ronald L. Rivest , 2001
"... This paper introduces a new representation for Boolean functions, called decision lists, and shows that they are efficiently learnable from examples. More precisely, this result is established for \k-DL" { the set of decision lists with conjunctive clauses of size k at each decision. Since k ..."
Abstract - Cited by 427 (0 self) - Add to MetaCart
This paper introduces a new representation for Boolean functions, called decision lists, and shows that they are efficiently learnable from examples. More precisely, this result is established for \k-DL" { the set of decision lists with conjunctive clauses of size k at each decision. Since

A comparative study of energy minimization methods for Markov random fields

by Richard Szeliski, Ramin Zabih, Daniel Scharstein, Olga Veksler, Aseem Agarwala, Carsten Rother, et al. - IN ECCV , 2006
"... One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with some quantity such as depth or texture. While many such problems can be elegantly expressed in the language of Markov Ran ..."
Abstract - Cited by 415 (36 self) - Add to MetaCart
Random Fields (MRF’s), the resulting energy minimization problems were widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: for example, such methods form the basis for almost all the top-performing stereo methods

Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms

by Tom Leighton, Satish Rao - J. ACM , 1999
"... In this paper, we establish max-flow min-cut theorems for several important classes of multicommodity flow problems. In particular, we show that for any n-node multicommodity flow problem with uniform demands, the max-flow for the problem is within an O(log n) factor of the upper bound implied by ..."
Abstract - Cited by 357 (6 self) - Add to MetaCart
by the min-cut. The result (which is existentially optimal) establishes an important analogue of the famous 1-commodity max-flow min-cut theorem for problems with multiple commodities. The result also has substantial applications to the field of approximation algorithms. For example, we use the flow result

Cryptographic Limitations on Learning Boolean Formulae and Finite Automata

by Michael Kearns, Leslie Valiant - PROCEEDINGS OF THE TWENTY-FIRST ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING , 1989
"... In this paper we prove the intractability of learning several classes of Boolean functions in the distribution-free model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results are representation independent, in that they hold regardless of the syntact ..."
Abstract - Cited by 347 (14 self) - Add to MetaCart
In this paper we prove the intractability of learning several classes of Boolean functions in the distribution-free model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results are representation independent, in that they hold regardless

Topologically-aware overlay construction and server selection

by Sylvia Ratnasamy, Mark Handley, Richard Karp, Scott Shenker , 2002
"... A number of large-scale distributed Internet applications could potentially benefit from some level of knowledge about the relative proximity between its participating host nodes. For example, the performance of large overlay networks could be improved if the application-level connectivity between ..."
Abstract - Cited by 341 (3 self) - Add to MetaCart
A number of large-scale distributed Internet applications could potentially benefit from some level of knowledge about the relative proximity between its participating host nodes. For example, the performance of large overlay networks could be improved if the application-level connectivity

Operations for Learning with Graphical Models

by Wray L. Buntine - Journal of Artificial Intelligence Research , 1994
"... This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models ..."
Abstract - Cited by 276 (13 self) - Add to MetaCart
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models

A Theory of Networks for Approximation and Learning

by Tomaso Poggio, Federico Girosi - Laboratory, Massachusetts Institute of Technology , 1989
"... 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, t ..."
Abstract - Cited by 235 (24 self) - Add to MetaCart
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

The Ultimate Display

by Ivan E. Sutherland - Proceedings of the IFIP Congress , 1965
"... Office, ARPA, OSD We live in a physical world whose properties we have come to know well through long familiarity. We sense an involvement with this physical world which gives us the ability to predict its properties well. For example, we can predict where objects will fall, how well-known shapes lo ..."
Abstract - Cited by 232 (0 self) - Add to MetaCart
Office, ARPA, OSD We live in a physical world whose properties we have come to know well through long familiarity. We sense an involvement with this physical world which gives us the ability to predict its properties well. For example, we can predict where objects will fall, how well-known shapes
Results 1 - 10 of 5,770