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» Structured metric learning for high dimensional problems
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ICMLA
2008
13 years 11 months ago
Mapping Uncharted Waters: Exploratory Analysis, Visualization, and Clustering of Oceanographic Data
In this paper we describe an interdisciplinary collaboration between researchers in machine learning and oceanography. The collaboration was formed to study the problem of open oc...
Joshua M. Lewis, Pincelli M. Hull, Kilian Q. Weinb...
GECCO
2005
Springer
102views Optimization» more  GECCO 2005»
14 years 3 months ago
Latent variable crossover for k-tablet structures and its application to lens design problems
This paper presents the Real-coded Genetic Algorithms for high-dimensional ill-scaled structures, what is called, the ktablet structure. The k-tablet structure is the landscape th...
Jun Sakuma, Shigenobu Kobayashi
IDA
2003
Springer
14 years 3 months ago
A Semi-supervised Method for Learning the Structure of Robot Environment Interactions
For a mobile robot to act autonomously, it must be able to construct a model of its interaction with the environment. Oates et al. developed an unsupervised learning method that pr...
Axel Großmann, Matthias Wendt, Jeremy Wyatt
ICML
2010
IEEE
13 years 11 months ago
From Transformation-Based Dimensionality Reduction to Feature Selection
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy
MICAI
2004
Springer
14 years 3 months ago
Faster Proximity Searching in Metric Data
A number of problems in computer science can be solved efficiently with the so called memory based or kernel methods. Among this problems (relevant to the AI community) are multime...
Edgar Chávez, Karina Figueroa