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KDD
2007
ACM
177views Data Mining» more  KDD 2007»
14 years 10 months ago
Mining optimal decision trees from itemset lattices
We present DL8, an exact algorithm for finding a decision tree that optimizes a ranking function under size, depth, accuracy and leaf constraints. Because the discovery of optimal...
Élisa Fromont, Siegfried Nijssen
KDD
2006
ACM
134views Data Mining» more  KDD 2006»
14 years 10 months ago
Learning to rank networked entities
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections...
Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal
KDD
2006
ACM
180views Data Mining» more  KDD 2006»
14 years 10 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
KDD
2006
ACM
191views Data Mining» more  KDD 2006»
14 years 10 months ago
Beyond classification and ranking: constrained optimization of the ROI
Classification has been commonly used in many data mining projects in the financial service industry. For instance, to predict collectability of accounts receivable, a binary clas...
Lian Yan, Patrick Baldasare
KDD
2001
ACM
163views Data Mining» more  KDD 2001»
14 years 10 months ago
Learning to recognize brain specific proteins based on low-level features from on-line prediction servers
During the last decade, the area of bioinformatics has produced an overwhelming amount of data, with the recently published draft of the human genome being the most prominent exam...
Henrik Boström, Joakim Cöster, Lars Aske...
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