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ML
2008
ACM
15 years 1 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
ICASSP
2011
IEEE
14 years 5 months ago
Learning and inference algorithms for partially observed structured switching vector autoregressive models
We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...
Balakrishnan Varadarajan, Sanjeev Khudanpur
ICIP
2007
IEEE
16 years 3 months ago
Large Scale Learning of Active Shape Models
We propose a framework to learn statistical shape models for faces as piecewise linear models. Specifically, our methodology builds upon primitive active shape models(ASM) to hand...
Atul Kanaujia, Dimitris N. Metaxas
NIPS
2001
15 years 2 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr
SAB
2010
Springer
150views Optimization» more  SAB 2010»
14 years 11 months ago
Internal Models in the Cerebellum: A Coupling Scheme for Online and Offline Learning in Procedural Tasks
The cerebellum plays a major role in motor control. It is thought to mediate the acquisition of forward and inverse internal models of the bodyenvironment interaction [1]. In this ...
Jean-Baptiste Passot, Niceto Luque, Angelo Arleo