Results 11 - 20 of 145,574

Fast algorithms for large-state-space HMMs with applications to web usage analysis

by Pedro F Felzenszwalb , Daniel P Huttenlocher , Jon M Kleinberg - In Neural Information and Processing Systems , 2003
"... Abstract In applying Hidden Markov Models to the analysis of massive data streams, it is often necessary to use an artificially reduced set of states; this is due in large part to the fact that the basic HMM estimation algorithms have a quadratic dependence on the size of the state set. We present ..."
Abstract - Cited by 32 (5 self) - Add to MetaCart
Abstract In applying Hidden Markov Models to the analysis of massive data streams, it is often necessary to use an artificially reduced set of states; this is due in large part to the fact that the basic HMM estimation algorithms have a quadratic dependence on the size of the state set. We present

Bandit based Monte-Carlo Planning

by Levente Kocsis, Csaba Szepesvári - In: ECML-06. Number 4212 in LNCS , 2006
"... Abstract. For large state-space Markovian Decision Problems Monte-Carlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide Monte-Carlo planning. In finite-horizon or discounted MDPs the algo ..."
Abstract - Cited by 446 (7 self) - Add to MetaCart
Abstract. For large state-space Markovian Decision Problems Monte-Carlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new algorithm, UCT, that applies bandit ideas to guide Monte-Carlo planning. In finite-horizon or discounted MDPs

Large margin methods for structured and interdependent output variables

by Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun - JOURNAL OF MACHINE LEARNING RESEARCH , 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
Abstract - Cited by 624 (12 self) - Add to MetaCart
Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses

Large N field theories, string theory and gravity

by Ofer Aharony, Steven S. Gubser, Juan Maldacena, Hirosi Ooguri, Yaron Oz , 2001
"... We review the holographic correspondence between field theories and string/M theory, focusing on the relation between compactifications of string/M theory on Anti-de Sitter spaces and conformal field theories. We review the background for this correspondence and discuss its motivations and the evide ..."
Abstract - Cited by 1443 (45 self) - Add to MetaCart
We review the holographic correspondence between field theories and string/M theory, focusing on the relation between compactifications of string/M theory on Anti-de Sitter spaces and conformal field theories. We review the background for this correspondence and discuss its motivations

The large N limit of superconformal field theories and supergravity

by Juan Maldacena , 1998
"... We show that the large N limit of certain conformal field theories in various dimensions include in their Hilbert space a sector describing supergravity on the product of AntideSitter spacetimes, spheres and other compact manifolds. This is shown by taking some branes in the full M/string theory and ..."
Abstract - Cited by 5631 (20 self) - Add to MetaCart
We show that the large N limit of certain conformal field theories in various dimensions include in their Hilbert space a sector describing supergravity on the product of AntideSitter spacetimes, spheres and other compact manifolds. This is shown by taking some branes in the full M/string theory

Large Margin Classification Using the Perceptron Algorithm

by Yoav Freund, Robert E. Schapire - Machine Learning , 1998
"... We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik 's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large ..."
Abstract - Cited by 521 (2 self) - Add to MetaCart
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

Imagenet: A large-scale hierarchical image database

by Jia Deng, Wei Dong, Richard Socher, Li-jia Li, Kai Li, Li Fei-fei - In CVPR , 2009
"... The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce her ..."
Abstract - Cited by 840 (28 self) - Add to MetaCart
of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image

Resource-Aware Verification Using Randomized Exploration of Large State Spaces

by Nazha Abed, Stavros Tripakis - In SPIN’08, number 5156 in LNCS , 2008
"... Abstract. Exhaustive verification often suffers from the state-explosion problem, where the reachable state space is too large to fit in main memory. For this reason, and because of disk swapping, once the main memory is full very little progress is made, and the process is not scalable. To alleviat ..."
Abstract - Cited by 4 (2 self) - Add to MetaCart
Abstract. Exhaustive verification often suffers from the state-explosion problem, where the reachable state space is too large to fit in main memory. For this reason, and because of disk swapping, once the main memory is full very little progress is made, and the process is not scalable

Minimization of Large State Spaces using Symbolic Branching Bisimulation

by Ralf Wimmer, Marc Herbstritt, Bernd Becker - In Proc. of DDECS’06 , 2006
"... Abstract: Bisimulations in general are a powerful concept to minimize large finite state systems regarding some well-defined observational behavior. In contrast to strong bisimulation, for branching bisimulation there are only tools available that work on an explicit state space representation. In t ..."
Abstract - Cited by 4 (4 self) - Add to MetaCart
Abstract: Bisimulations in general are a powerful concept to minimize large finite state systems regarding some well-defined observational behavior. In contrast to strong bisimulation, for branching bisimulation there are only tools available that work on an explicit state space representation

Pregel: A system for large-scale graph processing

by Grzegorz Malewicz, Matthew H. Austern, Aart J. C. Bik, James C. Dehnert, Ilan Horn, Naty Leiser, Grzegorz Czajkowski - IN SIGMOD , 2010
"... Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs—in some cases billions of vertices, trillions of edges—poses challenges to their efficient processing. In this paper we present a computational model ..."
Abstract - Cited by 496 (0 self) - Add to MetaCart
Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs—in some cases billions of vertices, trillions of edges—poses challenges to their efficient processing. In this paper we present a computational
Results 11 - 20 of 145,574