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Improved Boosting Algorithms Using Confidencerated Predictions
 MACHINE LEARNING
, 1999
"... We describe several improvements to Freund and Schapire’s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find impr ..."
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Cited by 933 (26 self)
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We describe several improvements to Freund and Schapire’s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find
Randomized Algorithms
, 1995
"... Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available, or the simp ..."
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Cited by 2210 (37 self)
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, or the simplest, or both. A randomized algorithm is an algorithm that uses random numbers to influence the choices it makes in the course of its computation. Thus its behavior (typically quantified as running time or quality of output) varies from
Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
 Journal of the ACM
, 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
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Cited by 1231 (13 self)
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We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
The Viterbi algorithm
 Proceedings of the IEEE
, 1973
"... vol. 6, no. 8, pp. 211220, 1951. [7] J. L. Anderson and J. W..Ryon, “Electromagnetic radiation in accelerated systems, ” Phys. Rev., vol. 181, pp. 17651775, 1969. [8] C. V. Heer, “Resonant frequencies of an electromagnetic cavity in an accelerated system of reference, ” Phys. Reu., vol. 134, pp. A ..."
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Cited by 985 (3 self)
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vol. 6, no. 8, pp. 211220, 1951. [7] J. L. Anderson and J. W..Ryon, “Electromagnetic radiation in accelerated systems, ” Phys. Rev., vol. 181, pp. 17651775, 1969. [8] C. V. Heer, “Resonant frequencies of an electromagnetic cavity in an accelerated system of reference, ” Phys. Reu., vol. 134, pp. A799A804, 1964. [9] T. C. Mo, “Theory of electrodynamics in media in noninertial frames and applications, ” J. Math. Phys., vol. 11, pp. 25892610, 1970.
Large Margin Classification Using the Perceptron Algorithm
 Machine Learning
, 1998
"... We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leaveoneout method. Like Vapnik 's maximalmargin classifier, our algorithm takes advantage of data that are linearly separable with large ..."
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Cited by 518 (2 self)
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with large margins. Compared to Vapnik's algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 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 ..."
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Cited by 524 (4 self)
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the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Paretooptimal 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
On Spectral Clustering: Analysis and an algorithm
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 2001
"... Despite many empirical successes of spectral clustering methods  algorithms that cluster points using eigenvectors of matrices derived from the distances between the points  there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
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Cited by 1697 (13 self)
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Despite many empirical successes of spectral clustering methods  algorithms that cluster points using eigenvectors of matrices derived from the distances between the points  there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors
Instancebased learning algorithms
 Machine Learning
, 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
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Cited by 1359 (18 self)
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Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances
An Algorithm for Tracking Multiple Targets
 IEEE Transactions on Automatic Control
, 1979
"... Abstract—An algorithm for tracking multiple targets In a cluttered algorithms. Clustering is the process of dividing the entire environment Is developed. The algorithm Is capable of Initiating tracks, set of targets and measurements into independent groups accounting for false or m[~clngreports, and ..."
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Cited by 586 (0 self)
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target, or that the measurement Is false. nally, it is desirable for an algorithm to be recursive so Target states are estimated from each such da*aas.soclatloo hypothesis that all the previous data do not have to be reprocessed using a 1C~InlQnfilter. As mere measurements are received, the probabill
Results 1  10
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