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147
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ICML
1999
IEEE
16 years 3 months ago
Abstracting from Robot Sensor Data using Hidden Markov Models
ing from Robot Sensor Data using Hidden Markov Models Laura Firoiu, Paul Cohen Computer Science Department, LGRC University of Massachusetts at Amherst, Box 34610 Amherst, MA 01003...
Laura Firoiu, Paul R. Cohen
145
Voted
ICML
2010
IEEE
15 years 3 months ago
A Theoretical Analysis of Feature Pooling in Visual Recognition
Many modern visual recognition algorithms incorporate a step of spatial `pooling', where the outputs of several nearby feature detectors are combined into a local or global `...
Y-Lan Boureau, Jean Ponce, Yann LeCun
118
Voted
CORR
2010
Springer
104views Education» more  CORR 2010»
15 years 2 months ago
Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory
This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system. From the signature images, global and local features are extracted and ...
Dakshina Ranjan Kisku, Phalguni Gupta, Jamuna Kant...
127
Voted
BMCBI
2008
173views more  BMCBI 2008»
15 years 2 months ago
Improved machine learning method for analysis of gas phase chemistry of peptides
Background: Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) o...
Allison Gehrke, Shaojun Sun, Lukasz A. Kurgan, Nat...
126
Voted
JUCS
2006
185views more  JUCS 2006»
15 years 2 months ago
The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States
We outline the Berlin Brain-Computer Interface (BBCI), a system which enables us to translate brain signals from movements or movement intentions into control commands. The main co...
Benjamin Blankertz, Guido Dornhege, Steven Lemm, M...