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Convergent Tree-reweighted Message Passing for Energy Minimization

by Vladimir Kolmogorov - ACCEPTED TO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (PAMI), 2006. ABSTRACTACCEPTED TO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (PAMI) , 2006
"... Algorithms for discrete energy minimization are of fundamental importance in computer vision. In this paper we focus on the recent technique proposed by Wainwright et al. [33]- tree-reweighted max-product message passing (TRW). It was inspired by the problem of maximizing a lower bound on the energy ..."
Abstract - Cited by 489 (16 self) - Add to MetaCart
Algorithms for discrete energy minimization are of fundamental importance in computer vision. In this paper we focus on the recent technique proposed by Wainwright et al. [33]- tree-reweighted max-product message passing (TRW). It was inspired by the problem of maximizing a lower bound

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

by Jiawei Han, Jian Pei, Yiwen Yin, Runying Mao - DATA MINING AND KNOWLEDGE DISCOVERY , 2004
"... Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still co ..."
Abstract - Cited by 1752 (64 self) - Add to MetaCart
- based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our

A tutorial on support vector machines for pattern recognition

by Christopher J. C. Burges - Data Mining and Knowledge Discovery , 1998
"... The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SV ..."
Abstract - Cited by 3393 (12 self) - Add to MetaCart
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when

Minimization of Tree Pattern Queries

by Sihem Amer-yahia, Sungran Cho, Laks V.S. Lakshmanan, Divesh Srivastava - In SIGMOD , 2001
"... Tree patterns form a natural basis to query tree-structured data such as XML and LDAP. Since the efficiency of tree pattern matching against a tree-structured database depends on the size of the pattern, it is essential to identify and eliminate redundant nodes in the pattern and do so as quickly as ..."
Abstract - Cited by 137 (4 self) - Add to MetaCart
on propagating "information labels" up the tree pattern. CDM can be applied prior to ACIM for improving the minimization efficiency. We complement our analytical results with an experimental study that shows the effectiveness of our tree pattern minimization techniques. 1.

Inducing Features of Random Fields

by Stephen Della Pietra, Vincent Della Pietra, John Lafferty - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract - Cited by 670 (10 self) - Add to MetaCart
We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing

Gradient-based learning applied to document recognition

by Yann Lecun, Léon Bottou, Yoshua Bengio, Patrick Haffner - Proceedings of the IEEE , 1998
"... Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify hi ..."
Abstract - Cited by 1533 (84 self) - Add to MetaCart
high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed

Features of similarity.

by Amos Tversky - Psychological Review , 1977
"... Similarity plays a fundamental role in theories of knowledge and behavior. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. Indeed, the concept of similarity is ubiquitous in psychological theory. It underlies the accounts of stimu ..."
Abstract - Cited by 1455 (2 self) - Add to MetaCart
of stimulus and response generalization in learning, it is employed to explain errors in memory and pattern recognition, and it is central to the analysis of connotative meaning. Similarity or dissimilarity data appear in di¤erent forms: ratings of pairs, sorting of objects, communality between associations

GRASP - A New Search Algorithm for Satisfiability

by Joso L Marques Silva , 1996
"... This paper introduces GRASP (Generic seaRch Algorithm for the Satisjiability Problem), an integrated algorithmic framework for SAT that un.$es several previously proposed searchpruning techniques and facilitates ident$cation of additional ones. GRASP is premised on the inevitability of confzicts dur ..."
Abstract - Cited by 449 (34 self) - Add to MetaCart
that are necessary for a solution to be found. fiperimental results obtained from a large number of benchmarks, including many from the $eld of test pattern generation, indicate that application of the proposed confzict analysis techniques to SATalgorithm can be extremely effectivefor a large number

Tree Pattern Query Minimization

by Sihem Amer-yahia, Sungran Cho, Laks V. S. Lakshmanan, Divesh Srivastava
"... Tree patterns form a natural basis to query treestructured data such as XML and LDAP. To improve the efficiency of tree pattern matching, it is essential to quickly identify and eliminate redundant nodes in the pattern. In this paper, we study tree pattern minimization both in the absence and in the ..."
Abstract - Cited by 58 (0 self) - Add to MetaCart
analytical results with an experimental study that shows the effectiveness of our tree pattern minimization techniques. Keywords: tree patterns, XML, query minimization 1

An Efficient k-Means Clustering Algorithm: Analysis and Implementation

by Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko, Ruth Silverman, Angela Y. Wu , 2000
"... K-means 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 ..."
Abstract - Cited by 417 (4 self) - Add to MetaCart
K-means 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 1 - 10 of 3,889