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NIPS
2001
13 years 5 months ago
Incremental A*
Sven Koenig, Maxim Likhachev
NIPS
2001
13 years 5 months ago
Natural Language Grammar Induction Using a Constituent-Context Model
This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural language text. Most previous work has focused on maximizing likelihood according...
Dan Klein, Christopher D. Manning
NIPS
2001
13 years 5 months ago
Online Learning with Kernels
Abstract--Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is availab...
Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
NIPS
2001
13 years 5 months ago
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...
Roni Khardon, Dan Roth, Rocco A. Servedio
NIPS
2001
13 years 5 months ago
Fragment Completion in Humans and Machines
Partial information can trigger a complete memory. At the same time, human memory is not perfect. A cue can contain enough information to specify an item in memory, but fail to tr...
David Jacobs, Bas Rokers, Archisman Rudra, Zili Li...
NIPS
2001
13 years 5 months ago
Active Information Retrieval
When a client interacts with an expert, e.g. a doctor, it falls upon the expert to ask questions that steer the process towards fulfilling the client's needs. This is most ef...
Tommi Jaakkola, Hava T. Siegelmann
NIPS
2001
13 years 5 months ago
Information Geometrical Framework for Analyzing Belief Propagation Decoder
The mystery of belief propagation (BP) decoder, especially of the turbo decoding, is studied from information geometrical viewpoint. The loopy belief network (BN) of turbo codes m...
Shiro Ikeda, Toshiyuki Tanaka, Shun-ichi Amari
NIPS
2001
13 years 5 months ago
Distribution of Mutual Information
Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliabilit...
M. Hutter