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» Desirable Characteristics of Learning Companions
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ICES
1998
Springer
78views Hardware» more  ICES 1998»
13 years 10 months ago
Intrinsic Circuit Evolution Using Programmable Analogue Arrays
The basic properties of programmable analogue arrays are described and the problem of quantifying the fitness of an analogue circuit is discussed. A set of blocks appropriate for u...
Stuart J. Flockton, Kevin Sheehan
TPHOL
2008
IEEE
14 years 16 days ago
The Isabelle Framework
g to the well-known “LCF approach” of secure inferences as abstract datatype constructors in ML [16]; explicit proof terms are also available [8]. Isabelle/Isar provides sophis...
Makarius Wenzel, Lawrence C. Paulson, Tobias Nipko...
ICML
2001
IEEE
14 years 7 months ago
A Unified Loss Function in Bayesian Framework for Support Vector Regression
In this paper, we propose a unified non-quadratic loss function for regression known as soft insensitive loss function (SILF). SILF is a flexible model and possesses most of the d...
Wei Chu, S. Sathiya Keerthi, Chong Jin Ong
NIPS
2007
13 years 7 months ago
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems
Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learn...
Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
11 years 8 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