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CORR
2010
Springer
228views Education» more  CORR 2010»
13 years 7 months ago
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse c...
Katya Scheinberg, Shiqian Ma, Donald Goldfarb
ICML
2009
IEEE
14 years 9 months ago
Robust bounds for classification via selective sampling
We introduce a new algorithm for binary classification in the selective sampling protocol. Our algorithm uses Regularized Least Squares (RLS) as base classifier, and for this reas...
Nicolò Cesa-Bianchi, Claudio Gentile, Franc...
WSDM
2009
ACM
125views Data Mining» more  WSDM 2009»
14 years 3 months ago
Less is more: sampling the neighborhood graph makes SALSA better and faster
In this paper, we attempt to improve the effectiveness and the efficiency of query-dependent link-based ranking algorithms such as HITS, MAX and SALSA. All these ranking algorith...
Marc Najork, Sreenivas Gollapudi, Rina Panigrahy
CIKM
2010
Springer
13 years 7 months ago
Ranking under temporal constraints
This paper introduces the notion of temporally constrained ranked retrieval, which, given a query and a time constraint, produces the best possible ranked list within the specifi...
Lidan Wang, Donald Metzler, Jimmy Lin
ISNN
2004
Springer
14 years 2 months ago
Sparse Bayesian Learning Based on an Efficient Subset Selection
Based on rank-1 update, Sparse Bayesian Learning Algorithm (SBLA) is proposed. SBLA has the advantages of low complexity and high sparseness, being very suitable for large scale pr...
Liefeng Bo, Ling Wang, Licheng Jiao