Sciweavers

ICPR
2000
IEEE
13 years 9 months ago
Scaling-Up Support Vector Machines Using Boosting Algorithm
In the recent years support vector machines (SVMs) have been successfully applied to solve a large number of classification problems. Training an SVM, usually posed as a quadrati...
Dmitry Pavlov, Jianchang Mao, Byron Dom
ICPP
2000
IEEE
13 years 9 months ago
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...
Harsha S. Nagesh, Sanjay Goil, Alok N. Choudhary
VLDB
2001
ACM
82views Database» more  VLDB 2001»
13 years 9 months ago
Supporting Incremental Join Queries on Ranked Inputs
This paper investigates the problem of incremental joins of multiple ranked data sets when the join condition is a list of arbitrary user-defined predicates on the input tuples. ...
Apostol Natsev, Yuan-Chi Chang, John R. Smith, Chu...
SOFSEM
2001
Springer
13 years 9 months ago
How Can Computer Science Contribute to Knowledge Discovery?
Knowledge discovery, that is, to analyze a given massive data set and derive or discover some knowledge from it, has been becoming a quite important subject in several fields incl...
Osamu Watanabe
MICCAI
2001
Springer
13 years 9 months ago
Mass Preserving Mappings and Image Registration
Image registration is the process of establishing a common geometric reference frame between two or more data sets from the same or different imaging modalities possibly taken at ...
Steven Haker, Allen Tannenbaum, Ron Kikinis
ICANN
2001
Springer
13 years 9 months ago
Independent Variable Group Analysis
Humans tend to group together related properties in order to understand complex phenomena. When modeling large problems with limited representational resources, it is important to...
Krista Lagus, Esa Alhoniemi, Harri Valpola
WAIM
2009
Springer
13 years 9 months ago
Kernel-Based Transductive Learning with Nearest Neighbors
In the k-nearest neighbor (KNN) classifier, nearest neighbors involve only labeled data. That makes it inappropriate for the data set that includes very few labeled data. In this ...
Liangcai Shu, Jinhui Wu, Lei Yu, Weiyi Meng
GECCO
2009
Springer
110views Optimization» more  GECCO 2009»
13 years 10 months ago
EMO shines a light on the holes of complexity space
Typical domains used in machine learning analyses only partially cover the complexity space, remaining a large proportion of problem difficulties that are not tested. Since the ac...
Núria Macià, Albert Orriols-Puig, Es...
FQAS
2009
Springer
142views Database» more  FQAS 2009»
13 years 10 months ago
On the Selection of the Best Retrieval Result Per Query - An Alternative Approach to Data Fusion
Some recent works have shown that the “perfect” selection of the best IR system per query could lead to a significant improvement on the retrieval performance. Motivated by thi...
Antonio Juárez-González, Manuel Mont...
CIARP
2009
Springer
13 years 10 months ago
Neural Network Ensembles from Training Set Expansions
Abstract. In this work we propose a new method to create neural network ensembles. Our methodology develops over the conventional technique of bagging, where multiple classifiers ...
Debrup Chakraborty