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» Evaluating learning algorithms and classifiers
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141
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STOC
1999
ACM
122views Algorithms» more  STOC 1999»
15 years 9 months ago
Oblivious Transfer and Polynomial Evaluation
Oblivious polynomial evaluation is a protocol involving two parties, a sender whose input is a polynomial P, and a receiver whose input is a value α. At the end of the protocol t...
Moni Naor, Benny Pinkas
PRL
2011
14 years 7 months ago
A Bayes-true data generator for evaluation of supervised and unsupervised learning methods
Benchmarking pattern recognition, machine learning and data mining methods commonly relies on real-world data sets. However, there are some disadvantages in using real-world data....
Janick V. Frasch, Aleksander Lodwich, Faisal Shafa...
ALT
2004
Springer
15 years 8 months ago
Applications of Regularized Least Squares to Classification Problems
Abstract. We present a survey of recent results concerning the theoretical and empirical performance of algorithms for learning regularized least-squares classifiers. The behavior ...
Nicolò Cesa-Bianchi
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
13 years 7 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman
BMCBI
2008
106views more  BMCBI 2008»
15 years 4 months ago
A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selec
Background: Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnologica...
Ning Wei, Erwin Flaschel, Karl Friehs, Tim W. Natt...