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ML
2008
ACM
15 years 6 months ago
A bias/variance decomposition for models using collective inference
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
Jennifer Neville, David Jensen
NECO
2008
101views more  NECO 2008»
15 years 6 months ago
On the Classification Capability of Sign-Constrained Perceptrons
The perceptron (also referred to as McCulloch-Pitts neuron, or linear threshold gate) is commonly used as a simplified model for the discrimination and learning capability of a bi...
Robert A. Legenstein, Wolfgang Maass
CAI
2010
Springer
15 years 4 months ago
SMA - The Smyle Modeling Approach
Abstract. This paper introduces the model-based software development lifecycle model SMA--the Smyle Modeling Approach--which is centered around Smyle. Smyle is a dedicated learning...
Benedikt Bollig, Joost-Pieter Katoen, Carsten Kern...
ICCV
1999
IEEE
15 years 11 months ago
Principal Manifolds and Bayesian Subspaces for Visual Recognition
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Three techniques: Principal Component Analy...
Baback Moghaddam
TIT
2008
76views more  TIT 2008»
15 years 6 months ago
Improved Risk Tail Bounds for On-Line Algorithms
We prove the strongest known bound for the risk of hypotheses selected from the ensemble generated by running a learning algorithm incrementally on the training data. Our result i...
Nicolò Cesa-Bianchi, Claudio Gentile