Results 1 - 10 of 64,459

Optimal Brain Damage

by Yann Le Cun, John S. Denker, Sara A. Sola , 1990
"... We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved sp ..."
Abstract - Cited by 510 (5 self) - Add to MetaCart
We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved

Some optimal inapproximability results

by Johan Håstad , 2002
"... We prove optimal, up to an arbitrary ffl? 0, inapproximability results for Max-Ek-Sat for k * 3, maximizing the number of satisfied linear equations in an over-determined system of linear equations modulo a prime p and Set Splitting. As a consequence of these results we get improved lower bounds for ..."
Abstract - Cited by 751 (11 self) - Add to MetaCart
We prove optimal, up to an arbitrary ffl? 0, inapproximability results for Max-Ek-Sat for k * 3, maximizing the number of satisfied linear equations in an over-determined system of linear equations modulo a prime p and Set Splitting. As a consequence of these results we get improved lower bounds

Global Optimization with Polynomials and the Problem of Moments

by Jean B. Lasserre - SIAM JOURNAL ON OPTIMIZATION , 2001
"... We consider the problem of finding the unconstrained global minimum of a real-valued polynomial p(x) : R R, as well as the global minimum of p(x), in a compact set K defined by polynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear ma ..."
Abstract - Cited by 577 (48 self) - Add to MetaCart
We consider the problem of finding the unconstrained global minimum of a real-valued polynomial p(x) : R R, as well as the global minimum of p(x), in a compact set K defined by polynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear

Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization

by Carlos M. Fonseca, Peter J. Fleming , 1993
"... The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
Abstract - Cited by 633 (15 self) - Add to MetaCart
The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified

An extended set of Haar-like features for rapid objection detection

by Rainer Lienhart, Jochen Maydt - IEEE ICIP
"... Recently Viola et al. [5] have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated haar-like features. These novel features significantly enrich the simple features of [5] and can also be calculated ef ..."
Abstract - Cited by 577 (4 self) - Add to MetaCart
Recently Viola et al. [5] have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated haar-like features. These novel features significantly enrich the simple features of [5] and can also be calculated

Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

by N. Srinivas, Kalyanmoy Deb - Evolutionary Computation , 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
Abstract - Cited by 539 (5 self) - Add to MetaCart
the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a

Exact Matrix Completion via Convex Optimization

by Emmanuel J. Candès, Benjamin Recht , 2008
"... We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfe ..."
Abstract - Cited by 873 (26 self) - Add to MetaCart
perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys m ≥ C n 1.2 r log n for some positive numerical constant C, then with very high probability, most n × n matrices of rank r can be perfectly recovered

Optimizing Search Engines using Clickthrough Data

by Thorsten Joachims , 2002
"... This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches ..."
Abstract - Cited by 1314 (23 self) - Add to MetaCart
This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous

The pyramid match kernel: Discriminative classification with sets of image features

by Kristen Grauman, Trevor Darrell - IN ICCV , 2005
"... Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondenc ..."
Abstract - Cited by 544 (29 self) - Add to MetaCart
Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve

Toward optimal feature selection

by Daphne Koller, Mehran Sahami - In 13th International Conference on Machine Learning , 1995
"... In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it g ..."
Abstract - Cited by 480 (9 self) - Add to MetaCart
In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature
Results 1 - 10 of 64,459