Results 1  10
of
638,548
Fast Algorithms for Mining Association Rules
, 1994
"... We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known a ..."
Abstract

Cited by 3548 (15 self)
 Add to MetaCart
We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known
A Fast Algorithm for Particle Simulations
, 1987
"... this paper to the case where the potential (or force) at a point is a sum of pairwise An algorithm is presented for the rapid evaluation of the potential and force fields in systems involving large numbers of particles interactions. More specifically, we consider potentials of whose interactions a ..."
Abstract

Cited by 1143 (19 self)
 Add to MetaCart
this paper to the case where the potential (or force) at a point is a sum of pairwise An algorithm is presented for the rapid evaluation of the potential and force fields in systems involving large numbers of particles interactions. More specifically, we consider potentials of whose interactions
FastMap: A Fast Algorithm for Indexing, DataMining and Visualization of Traditional and Multimedia Datasets
, 1995
"... A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in kd space, using k featureextraction functions, provided by a domain expert [25]. Thus, we can subsequently use highly finetuned spatial access methods (SAMs), to answer several types ..."
Abstract

Cited by 495 (23 self)
 Add to MetaCart
domain expert to assess the similarity/distance of two objects. Given only the distance information though, it is not obvious how to map objects into points. This is exactly the topic of this paper. We describe a fast algorithm to map objects into points in some kdimensional space (k is user
Sequential minimal optimization: A fast algorithm for training support vector machines
 Advances in Kernel MethodsSupport Vector Learning
, 1999
"... This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possi ..."
Abstract

Cited by 453 (3 self)
 Add to MetaCart
This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest
A Fast Algorithm for the Minimum Covariance Determinant Estimator
 Technometrics
, 1998
"... The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of multivariate location and scatter. Its objective is to find h observations (out of n) whose covariance matrix has the lowest determinant. Until now applications of the MCD were hampered by the comput ..."
Abstract

Cited by 338 (15 self)
 Add to MetaCart
variables. To deal with such problems we have developed a new algorithm for the MCD, called FASTMCD. The basic ideas are an inequality involving order statistics and determinants, and techniques which we call `selective iteration' and `nested extensions'. For small data sets FASTMCD typically
Simple fast algorithms for the editing distance between trees and related problems
 SIAM J. COMPUT
, 1989
"... Ordered labeled trees are trees in which the lefttoright order among siblings is. significant. The distance between two ordered trees is considered to be the weighted number of edit operations (insert, delete, and modify) to transform one tree to another. The problem of approximate tree matching i ..."
Abstract

Cited by 402 (12 self)
 Add to MetaCart
is also considered. Specifically, algorithms are designed to answer the following kinds of questions: 1. What is the distance between two trees? 2. What is the minimum distance between T and T when zero or more subtrees can be removed from T2 3. Let the pruning of a tree at node n mean removing all
Fast Algorithms for
, 1998
"... This paper develops fast algorithms for construction of a circulant modulated rate process to match with two primary traffic statistical functions: rate distribution f(x) and autocorrelation R(): Using existing modeling techniques, f(x) has to be limited to certain forms such as Gaussian or binomia ..."
Abstract
 Add to MetaCart
This paper develops fast algorithms for construction of a circulant modulated rate process to match with two primary traffic statistical functions: rate distribution f(x) and autocorrelation R(): Using existing modeling techniques, f(x) has to be limited to certain forms such as Gaussian
Fast probabilistic algorithms for verification of polynomial identities
 J. ACM
, 1980
"... ABSTRACT The starthng success of the RabmStrassenSolovay pnmahty algorithm, together with the intriguing foundattonal posstbthty that axtoms of randomness may constttute a useful fundamental source of mathemaucal truth independent of the standard axmmaUc structure of mathemaUcs, suggests a wgorous ..."
Abstract

Cited by 517 (1 self)
 Add to MetaCart
wgorous search for probabdisuc algonthms In dlustratmn of this observaUon, vanous fast probabdlsttc algonthms, with probability of correctness guaranteed a prion, are presented for testing polynomial ldentmes and propemes of systems of polynomials. Ancdlary fast algorithms for calculating resultants
A fast learning algorithm for deep belief nets
 Neural Computation
, 2006
"... We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in denselyconnected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a ..."
Abstract

Cited by 939 (49 self)
 Add to MetaCart
We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in denselyconnected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer
Results 1  10
of
638,548