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Factor Graphs and the Sum-Product Algorithm

by Frank R. Kschischang, Brendan J. Frey, Hans-Andrea Loeliger - IEEE TRANSACTIONS ON INFORMATION THEORY , 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
Abstract - Cited by 1791 (69 self) - Add to MetaCart
A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple

Unsupervised Models for Named Entity Classification

by Michael Collins, Yoram Singer - In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora , 1999
"... This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use of unlabe ..."
Abstract - Cited by 542 (4 self) - Add to MetaCart
of unlabeled data can reduce the requirements for supervision to just 7 simple “seed ” rules. The approach gains leverage from natural redundancy in the data: for many named-entity instances both the spelling of the name and the context in which it appears are sufficient to determine its type. We present two

Universally composable security: A new paradigm for cryptographic protocols

by Ran Canetti , 2013
"... We present a general framework for representing cryptographic protocols and analyzing their security. The framework allows specifying the security requirements of practically any cryptographic task in a unified and systematic way. Furthermore, in this framework the security of protocols is preserved ..."
Abstract - Cited by 833 (37 self) - Add to MetaCart
is preserved under a general protocol composition operation, called universal composition. The proposed framework with its security-preserving composition operation allows for modular design and analysis of complex cryptographic protocols from relatively simple building blocks. Moreover, within this framework

A learning algorithm for Boltzmann machines

by H. Ackley, E. Hinton, J. Sejnowski - Cognitive Science , 1985
"... The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a probl ..."
Abstract - Cited by 584 (13 self) - Add to MetaCart
The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a

Suffix arrays: A new method for on-line string searches

by Udi Manber, Gene Myers , 1991
"... A new and conceptually simple data structure, called a suffix array, for on-line string searches is intro-duced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees is that ..."
Abstract - Cited by 835 (0 self) - Add to MetaCart
A new and conceptually simple data structure, called a suffix array, for on-line string searches is intro-duced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees

Choices, values and frames.

by Daniel Kahneman - American Psychologist, , 1984
"... Making decisions is like speaking prose-people do it all the time, knowingly or unknowingly. It is hardly surprising, then, that the topic of decision making is shared by many disciplines, from mathematics and statistics, through economics and political science, to sociology and psychology. The stu ..."
Abstract - Cited by 684 (9 self) - Add to MetaCart
such as the weather or the opponent's resolve, the choice of an act may be construed as the acceptance of a gamble that can yield various outcomes with different probabilities. It is therefore natural that the study of decision making under risk has focused on choices between simple gambles with monetary

An analysis of Bayesian classifiers

by Pat Langley, Wayne Iba, Kevin Thompson - IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE , 1992
"... In this paper we present anaverage-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability that t ..."
Abstract - Cited by 440 (17 self) - Add to MetaCart
In this paper we present anaverage-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability

A framework for multiple-instance learning

by Oded Maron - In Advances in Neural Information Processing Systems , 1998
"... Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled ..."
Abstract - Cited by 264 (2 self) - Add to MetaCart
negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection

Generalizing from Simple Instances: An Uncomplicated Lesson from Kids Learning Object Categories

by Ji Y. Son, Linda B. Smith, Robert L. Goldstone
"... Abstraction is the process of stripping away irrelevant information so that learners can generalize on relevant similarities. Can we shortcut this process by directly teaching abstractions in the form of simplified instances? We tested this prediction in the domain of shape-based generalization and ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstraction is the process of stripping away irrelevant information so that learners can generalize on relevant similarities. Can we shortcut this process by directly teaching abstractions in the form of simplified instances? We tested this prediction in the domain of shape-based generalization

Learning to Order Things

by William W. Cohen, Robert E. Schapire, Yoram Singer - Journal of Artificial Intelligence Research , 1998
"... There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a ..."
Abstract - Cited by 409 (12 self) - Add to MetaCart
There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline
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