Results 1 - 10 of 21,683

Fitting a mixture model by expectation maximization to discover motifs in biopolymers.

by Timothy L Bailey , Charles Elkan - Proc Int Conf Intell Syst Mol Biol , 1994
"... Abstract The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expect~tiou ma.,dmization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model to th ..."
Abstract - Cited by 947 (5 self) - Add to MetaCart
Abstract The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expect~tiou ma.,dmization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model

A simple parallel algorithm for the maximal independent set problem

by Michael Luby - SIAM Journal on Computing , 1986
"... Simple parallel algorithms for the maximal independent set (MIS) problem are presented. The first algorithm is a Monte Carlo algorithm with a very local property. The local property of this algorithm may make it a useful protocol design tool in distributed computing environments and artificial intel ..."
Abstract - Cited by 450 (9 self) - Add to MetaCart
Simple parallel algorithms for the maximal independent set (MIS) problem are presented. The first algorithm is a Monte Carlo algorithm with a very local property. The local property of this algorithm may make it a useful protocol design tool in distributed computing environments and artificial

A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants

by Radford Neal, Geoffrey E. Hinton - Learning in Graphical Models , 1998
"... . The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the d ..."
Abstract - Cited by 993 (18 self) - Add to MetaCart
. The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect

A Simple Transmit Diversity Technique for Wireless Communications

by S. M. Alamouti , 1998
"... This paper presents a simple two-branch transmit diversity scheme. Using two transmit antennas and one receive antenna the scheme provides the same diversity order as maximal-ratio receiver combining (MRRC) with one transmit antenna, and two receive antennas. It is also shown that the scheme may ea ..."
Abstract - Cited by 2127 (0 self) - Add to MetaCart
This paper presents a simple two-branch transmit diversity scheme. Using two transmit antennas and one receive antenna the scheme provides the same diversity order as maximal-ratio receiver combining (MRRC) with one transmit antenna, and two receive antennas. It is also shown that the scheme may

The genetical evolution of social behaviour

by W D Hamilton - I. J. Theor. Biol. , 1964
"... A genetical mathematical model is described which allows for interactions between relatives on one another's fitness. Making use of Wright's Coefficient of Relationship as the measure of the proportion of replica genes in a relative, a quantity is found which incorporates the maximizing p ..."
Abstract - Cited by 932 (2 self) - Add to MetaCart
A genetical mathematical model is described which allows for interactions between relatives on one another's fitness. Making use of Wright's Coefficient of Relationship as the measure of the proportion of replica genes in a relative, a quantity is found which incorporates the maximizing

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
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

Algorithms for Non-negative Matrix Factorization

by Daniel D. Lee, H. Sebastian Seung - In NIPS , 2001
"... Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minim ..."
Abstract - Cited by 1246 (5 self) - Add to MetaCart
Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown

A training algorithm for optimal margin classifiers

by Bernhard E. Boser, et al. - PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY , 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
Abstract - Cited by 1865 (43 self) - Add to MetaCart
A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters

Energy Conserving Routing in Wireless Ad-hoc Networks

by Jae-hwan Chang, Leandros Tassiulas , 2000
"... An ad-hoc network of wireless static nodes is considered as it arises in a rapidly deployed, sensor based, monitoring system. Information is generated in certain nodes and needs to reach a set of designated gateway nodes. Each node may adjust its power within a certain range that determines the set ..."
Abstract - Cited by 622 (2 self) - Add to MetaCart
the set of possible one hop away neighbors. Traffic forwarding through multiple hops is employed when the intended destination is not within immediate reach. The nodes have limited initial amounts of energy that is consumed in different rates depending on the power level and the intended receiver. We

Those who understand: Knowledge growth in teaching. Educational Researcher

by Lee S. Shulman , 1986
"... "He who can, does. He who cannot, teaches. “ 1 don't know in what fit of pique George Bernard Shaw wrote that infamous aphorism, words that have plagued members of the teach-ing profession for nearly a century. They are found in "Maxims for Revolutionists, " an appendix to his pl ..."
Abstract - Cited by 1369 (1 self) - Add to MetaCart
"He who can, does. He who cannot, teaches. “ 1 don't know in what fit of pique George Bernard Shaw wrote that infamous aphorism, words that have plagued members of the teach-ing profession for nearly a century. They are found in "Maxims for Revolutionists, " an appendix to his
Results 1 - 10 of 21,683