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» Large-Scale Support Vector Learning with Structural Kernels
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JMLR
2006
186views more  JMLR 2006»
13 years 8 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
FPGA
2005
ACM
195views FPGA» more  FPGA 2005»
14 years 2 months ago
Sparse Matrix-Vector multiplication on FPGAs
Floating-point Sparse Matrix-Vector Multiplication (SpMXV) is a key computational kernel in scientific and engineering applications. The poor data locality of sparse matrices sig...
Ling Zhuo, Viktor K. Prasanna
ACCV
2009
Springer
14 years 21 days ago
Efficient Classification of Images with Taxonomies
We study the problem of classifying images into a given, pre-determined taxonomy. The task can be elegantly translated into the structured learning framework. Structured learning, ...
Alexander Binder, Motoaki Kawanabe, Ulf Brefeld
JMLR
2012
11 years 11 months ago
Sparse Additive Machine
We develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machine (SVM)...
Tuo Zhao, Han Liu
PR
2006
101views more  PR 2006»
13 years 8 months ago
Feature-based approach to semi-supervised similarity learning
For the management of digital document collections, automatic database analysis still has ties to deal with semantic queries and abstract concepts that users are looking for. When...
Philippe Henri Gosselin, Matthieu Cord