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» Optimization of Convex Risk Functions
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SIAMJO
2008
74views more  SIAMJO 2008»
13 years 10 months ago
Lagrangian-Dual Functions and Moreau--Yosida Regularization
In this paper, we consider the Lagrangian dual problem of a class of convex optimization problems. We first discuss the semismoothness of the Lagrangian-dual function . This prope...
Fanwen Meng, Gongyun Zhao, Mark Goh, Robert de Sou...
JMLR
2010
121views more  JMLR 2010»
13 years 5 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
NIPS
2004
13 years 11 months ago
The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space
A new distance measure between probability density functions (pdfs) is introduced, which we refer to as the Laplacian pdf distance. The Laplacian pdf distance exhibits a remarkabl...
Robert Jenssen, Deniz Erdogmus, José Carlos...
NAACL
2010
13 years 8 months ago
Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions
We describe a method of incorporating taskspecific cost functions into standard conditional log-likelihood (CLL) training of linear structured prediction models. Recently introduc...
Kevin Gimpel, Noah A. Smith
NIPS
2007
13 years 11 months ago
DIFFRAC: a discriminative and flexible framework for clustering
We present a novel linear clustering framework (DIFFRAC) which relies on a linear discriminative cost function and a convex relaxation of a combinatorial optimization problem. The...
Francis Bach, Zaïd Harchaoui