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ICDM
2007
IEEE
138views Data Mining» more  ICDM 2007»
14 years 3 months ago
Bandit-Based Algorithms for Budgeted Learning
We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is s...
Kun Deng, Chris Bourke, Stephen D. Scott, Julie Su...
BMCBI
2008
107views more  BMCBI 2008»
13 years 8 months ago
A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data
Background: A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughp...
Cong Zhou, Lucas D. Bowler, Jianfeng Feng
ICASSP
2011
IEEE
13 years 13 days ago
Feature selection based on Multiple Kernel Learning for single-channel sound source localization using the acoustic transfer fun
This paper presents a sound source (talker) localization method using only a single microphone. In our previous work [1], we discussed the single-channel sound source localization...
Ryoichi Takashima, Tetsuya Takiguchi, Yasuo Ariki
ROBIO
2006
IEEE
129views Robotics» more  ROBIO 2006»
14 years 2 months ago
Learning Utility Surfaces for Movement Selection
— Humanoid robots are highly redundant systems with respect to the tasks they are asked to perform. This redundancy manifests itself in the number of degrees of freedom of the ro...
Matthew Howard, Michael Gienger, Christian Goerick...
ACML
2009
Springer
14 years 22 days ago
Max-margin Multiple-Instance Learning via Semidefinite Programming
In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multipleinstance learning as a combinatorial maximum marg...
Yuhong Guo