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» Data Mining: Machine Learning, Statistics, and Databases
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139
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SDM
2004
SIAM
211views Data Mining» more  SDM 2004»
15 years 5 months ago
Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects
Databases are a key technology for molecular biology which is a very data intensive discipline. Since molecular biological databases are rather heterogeneous, unification and data...
Hans-Peter Kriegel, Peer Kröger, Alexey Pryak...
ICDM
2007
IEEE
138views Data Mining» more  ICDM 2007»
15 years 10 months ago
Bandit-Based Algorithms for Budgeted Learning
We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is s...
Kun Deng, Chris Bourke, Stephen D. Scott, Julie Su...
156
Voted
MIR
2006
ACM
141views Multimedia» more  MIR 2006»
15 years 9 months ago
Mining temporal patterns of movement for video content classification
Scalable approaches to video content classification are limited by an inability to automatically generate representations of events ode abstract temporal structure. This paper pre...
Michael Fleischman, Philip DeCamp, Deb Roy
111
Voted
KDD
2004
ACM
113views Data Mining» more  KDD 2004»
16 years 4 months ago
Learning spatially variant dissimilarity (SVaD) measures
Clustering algorithms typically operate on a feature vector representation of the data and find clusters that are compact with respect to an assumed (dis)similarity measure betwee...
Krishna Kummamuru, Raghu Krishnapuram, Rakesh Agra...
134
Voted
ICML
2000
IEEE
16 years 4 months ago
Meta-Learning by Landmarking Various Learning Algorithms
Landmarking is a novel approach to describing tasks in meta-learning. Previous approaches to meta-learning mostly considered only statistics-inspired measures of the data as a sou...
Bernhard Pfahringer, Hilan Bensusan, Christophe G....