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JMLR
2010
112views more  JMLR 2010»
13 years 2 months ago
Reduced-Rank Hidden Markov Models
Hsu et al. (2009) recently proposed an efficient, accurate spectral learning algorithm for Hidden Markov Models (HMMs). In this paper we relax their assumptions and prove a tighte...
Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon
KDD
2006
ACM
191views Data Mining» more  KDD 2006»
14 years 8 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
SIGIR
2010
ACM
13 years 11 months ago
How good is a span of terms?: exploiting proximity to improve web retrieval
Ranking search results is a fundamental problem in information retrieval. In this paper we explore whether the use of proximity and phrase information can improve web retrieval ac...
Krysta Marie Svore, Pallika H. Kanani, Nazan Khan
ICML
2006
IEEE
14 years 8 months ago
MISSL: multiple-instance semi-supervised learning
There has been much work on applying multiple-instance (MI) learning to contentbased image retrieval (CBIR) where the goal is to rank all images in a known repository using a smal...
Rouhollah Rahmani, Sally A. Goldman
ROCAI
2004
Springer
14 years 1 months ago
Learning Interestingness Measures in Terminology Extraction. A ROC-based approach
Abstract. In the field of Text Mining, a key phase in data preparation is concerned with the extraction of terms, i.e. collocation of words attached to specific concepts (e.g. Ph...
Mathieu Roche, Jérôme Azé, Yve...