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» Data mining, Hypergraph Transversals, and Machine Learning
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SBBD
2000
168views Database» more  SBBD 2000»
13 years 9 months ago
Fast Feature Selection Using Fractal Dimension
Dimensionalitycurse and dimensionalityreduction are two issues that have retained highinterest for data mining, machine learning, multimedia indexing, and clustering. We present a...
Caetano Traina Jr., Agma J. M. Traina, Leejay Wu, ...
ADMA
2010
Springer
271views Data Mining» more  ADMA 2010»
13 years 3 months ago
Exploiting Concept Clumping for Efficient Incremental E-Mail Categorization
We introduce a novel approach to incremental e-mail categorization based on identifying and exploiting "clumps" of messages that are classified similarly. Clumping reflec...
Alfred Krzywicki, Wayne Wobcke
ICMLA
2010
13 years 6 months ago
Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets
Abstract--Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as s...
Drausin Wulsin, Justin Blanco, Ram Mani, Brian Lit...
AAAI
2006
13 years 9 months ago
On Multi-Class Cost-Sensitive Learning
Rescaling is possibly the most popular approach to cost-sensitive learning. This approach works by rescaling the classes according to their costs, and it can be realized in differ...
Zhi-Hua Zhou, Xu-Ying Liu
KDD
2009
ACM
210views Data Mining» more  KDD 2009»
14 years 9 months ago
Large-scale behavioral targeting
Behavioral targeting (BT) leverages historical user behavior to select the ads most relevant to users to display. The state-of-the-art of BT derives a linear Poisson regression mo...
Ye Chen, Dmitry Pavlov, John F. Canny