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96,039
An improved master theorem for divideandconquer recurrences
 In Automata, languages and programming
, 1997
"... Abstract. This paper presents new theorems to analyze divideandconquer recurrences, which improve other similar ones in several aspects. In particular, these theorems provide more information, free us almost completely from technicalities like floors and ceilings, and cover a wider set of toll fun ..."
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Cited by 15 (2 self)
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Abstract. This paper presents new theorems to analyze divideandconquer recurrences, which improve other similar ones in several aspects. In particular, these theorems provide more information, free us almost completely from technicalities like floors and ceilings, and cover a wider set of toll
DivideandConquer Approximation Algorithms via Spreading Metrics
, 1996
"... We present a novel divideandconquer paradigm for approximating NPhard graph optimization problems. The paradigm models graph optimization problems that satisfy two properties: First, a divideandconquer approach is applicable. Second, a fractional spreading metric is computable in polynomial tim ..."
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Cited by 115 (10 self)
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We present a novel divideandconquer paradigm for approximating NPhard graph optimization problems. The paradigm models graph optimization problems that satisfy two properties: First, a divideandconquer approach is applicable. Second, a fractional spreading metric is computable in polynomial
Cut Problems And Their Application To DivideAndConquer
, 1996
"... INTRODUCTION 5.1 One of the most important paradigms in the design and analysis of algorithms is the notion of a divideandconquer algorithm. Every undergraduate course on algorithms teaches this method as one of its staples: to solve a problem quickly, one carefully splits the problem into two s ..."
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Cited by 84 (0 self)
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INTRODUCTION 5.1 One of the most important paradigms in the design and analysis of algorithms is the notion of a divideandconquer algorithm. Every undergraduate course on algorithms teaches this method as one of its staples: to solve a problem quickly, one carefully splits the problem into two
A training algorithm for optimal margin classifiers
 PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
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Cited by 1848 (44 self)
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is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leaveoneout method and the VC
A Scheme for RealTime Channel Establishment in WideArea Networks
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 1990
"... Multimedia communication involving digital audio and/or digital video has rather strict delay requirements. A realtime channel is defined in this paper as a simplex connection between a source and a destination characterized by parameters representing the performance requirements of the client. A r ..."
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Cited by 710 (31 self)
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Multimedia communication involving digital audio and/or digital video has rather strict delay requirements. A realtime channel is defined in this paper as a simplex connection between a source and a destination characterized by parameters representing the performance requirements of the client. A
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (15 self)
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upper bounds known today. We show that the number of hypotheses that are combined by our algorithm is the smallest number possible. Other outcomes of our analysis are results regarding the representational power of threshold circuits, the relation between learnability and compression, and a method
Exact Asymptotics of DivideandConquer Recurrences
"... The divideandconquer principle is a major paradigm of algorithms design. Corresponding cost functions satisfy recurrences that directly reflect the decomposition mechanism used in the algorithm. This work shows that periodicity phenomena, often of a fractal nature, are ubiquitous in the performanc ..."
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Cited by 8 (1 self)
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The divideandconquer principle is a major paradigm of algorithms design. Corresponding cost functions satisfy recurrences that directly reflect the decomposition mechanism used in the algorithm. This work shows that periodicity phenomena, often of a fractal nature, are ubiquitous
Convex Analysis
, 1970
"... In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a lo ..."
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Cited by 5350 (67 self)
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was the exploration of variations around a point, within the bounds imposed by the constraints, in order to help characterize solutions and portray them in terms of ‘variational principles’. Notions of perturbation, approximation and even generalized differentiability were extensively investigated. Variational theory
Multiplesize divideandconquer recurrences
 Also in Proceedings of the International Conference on Algorithms, the 1996 International Computer Symposium, 159161
, 1997
"... This note reports a tight asymptotic solution to the following recurrence on all positive integers n: where • α ≥ 0, β ≥ 0, c> 0, d> 0, ..."
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Cited by 2 (0 self)
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This note reports a tight asymptotic solution to the following recurrence on all positive integers n: where • α ≥ 0, β ≥ 0, c> 0, d> 0,
Evolving Neural Networks through Augmenting Topologies
 Evolutionary Computation
"... An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning task ..."
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Cited by 524 (113 self)
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that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering
Results 1  10
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96,039