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ICML
2005
IEEE
14 years 7 months ago
An efficient method for simplifying support vector machines
In this paper we describe a new method to reduce the complexity of support vector machines by reducing the number of necessary support vectors included in their solutions. The red...
DucDung Nguyen, Tu Bao Ho
ICML
2007
IEEE
14 years 7 months ago
Map building without localization by dimensionality reduction techniques
This paper proposes a new map building framework for mobile robot named Localization-Free Mapping by Dimensionality Reduction (LFMDR). In this framework, the robot map building is...
Takehisa Yairi
ICML
2005
IEEE
14 years 7 months ago
Supervised dimensionality reduction using mixture models
Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We...
Sajama, Alon Orlitsky
ICALT
2007
IEEE
13 years 6 months ago
Organizational Memory towards School Customs Constructing
School custom is the base of its sustainable development ability. Addressing the present dilemma of constructing the school customs, the paper argued school organizational knowled...
Yueliang Zhou, Jingyao Wang
NIPS
2004
13 years 8 months ago
Proximity Graphs for Clustering and Manifold Learning
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph with the input data points as...
Miguel Á. Carreira-Perpiñán, ...