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101
Voted
KDD
2005
ACM
165views Data Mining» more  KDD 2005»
16 years 28 days ago
Co-clustering by block value decomposition
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data a...
Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
16 years 28 days ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
KDD
2005
ACM
124views Data Mining» more  KDD 2005»
16 years 28 days ago
A multinomial clustering model for fast simulation of computer architecture designs
Computer architects utilize simulation tools to evaluate the merits of a new design feature. The time needed to adequately evaluate the tradeoffs associated with adding any new fe...
Kaushal Sanghai, Ting Su, Jennifer G. Dy, David R....
104
Voted
KDD
2004
ACM
154views Data Mining» more  KDD 2004»
16 years 28 days ago
Diagnosing extrapolation: tree-based density estimation
There has historically been very little concern with extrapolation in Machine Learning, yet extrapolation can be critical to diagnose. Predictor functions are almost always learne...
Giles Hooker
126
Voted
KDD
2004
ACM
135views Data Mining» more  KDD 2004»
16 years 28 days ago
Discovering additive structure in black box functions
Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that driv...
Giles Hooker