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959,649
TABU SEARCH
"... Tabu Search is a metaheuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. One of the main components of tabu search is its use of adaptive memory, which creates a more flexible search behavior. Memory based strategies are therefore the hallm ..."
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Cited by 788 (46 self)
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the hallmark of tabu search approaches, founded on a quest for "integrating principles, " by which alternative forms of memory are appropriately combined with effective strategies for exploiting them. In this chapter we address the problem of training multilayer feedforward neural networks
Sparse Bayesian Learning and the Relevance Vector Machine
, 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vect ..."
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Cited by 948 (5 self)
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This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classification tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance
A discriminatively trained, multiscale, deformable part model
 In IEEE Conference on Computer Vision and Pattern Recognition (CVPR2008
, 2008
"... This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a twofold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge ..."
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Cited by 551 (11 self)
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challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative
Maxmargin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
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Cited by 587 (15 self)
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In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from
MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS
 IEEE COMPUTER
, 2009
"... As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearestneighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels. Modern co ..."
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Cited by 548 (4 self)
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, recommender systems are based on one of two strategies. The content filtering approach creates a profile for each user or product to characterize its nature. For example, a movie profile could include attributes regarding its genre, the participating actors, its box office popularity, and so forth. User
Modeling and Forecasting Realized Volatility
, 2002
"... this paper is built. First, although raw returns are clearly leptokurtic, returns standardized by realized volatilities are approximately Gaussian. Second, although the distributions of realized volatilities are clearly rightskewed, the distributions of the logarithms of realized volatilities are a ..."
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Cited by 536 (50 self)
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: a fractionallyintegrated Gaussian vector autoregression (VAR) . Importantly, our approach explicitly permits measurement errors in the realized volatilities. Comparing the resulting volatility forecasts to those obtained from currently popular daily volatility models and more complicated high
Linear spatial pyramid matching using sparse coding for image classification
 in IEEE Conference on Computer Vision and Pattern Recognition(CVPR
, 2009
"... Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n 2 ∼ n 3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algo ..."
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Cited by 482 (19 self)
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Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n 2 ∼ n 3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup
GossipBased Computation of Aggregate Information
, 2003
"... between computers, and a resulting paradigm shift from centralized to highly distributed systems. With massive scale also comes massive instability, as node and link failures become the norm rather than the exception. For such highly volatile systems, decentralized gossipbased protocols are emergin ..."
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Cited by 457 (2 self)
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between computers, and a resulting paradigm shift from centralized to highly distributed systems. With massive scale also comes massive instability, as node and link failures become the norm rather than the exception. For such highly volatile systems, decentralized gossipbased protocols
Matchmaking: Distributed Resource Management for High Throughput Computing
 In Proceedings of the Seventh IEEE International Symposium on High Performance Distributed Computing
, 1998
"... Conventional resource management systems use a system model to describe resources and a centralized scheduler to control their allocation. We argue that this paradigm does not adapt well to distributed systems, particularly those built to support highthroughput computing. Obstacles include heteroge ..."
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Cited by 430 (20 self)
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approach to resource management in distributed environment with decentralized ownership of resources. Novel aspects of the framework include a semistructured data model that combines schema, data, and query in a simple but powerful specification language, and a clean separation of the matching
An Efficient kMeans Clustering Algorithm: Analysis and Implementation
, 2000
"... Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its ..."
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Cited by 406 (4 self)
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Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its
Results 11  20
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