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ICML
2005
IEEE
16 years 14 days ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
NN
1998
Springer
201views Neural Networks» more  NN 1998»
14 years 11 months ago
Neural mechanisms of selection and control of visually guided eye movements
The selection and control of action is a critical problem for both biological and machine animated systems that must operate in complex real world situations. Visually guided eye ...
Jeffrey D. Schall, Doug P. Hanes
104
Voted
CVPR
2005
IEEE
15 years 5 months ago
A Discriminative Framework for Modelling Object Classes
Here we explore a discriminative learning method on underlying generative models for the purpose of discriminating between object categories. Visual recognition algorithms learn m...
Alex Holub, Pietro Perona
GECCO
2009
Springer
204views Optimization» more  GECCO 2009»
15 years 4 months ago
Combined structure and motion extraction from visual data using evolutionary active learning
We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are c...
Krishnanand N. Kaipa, Josh C. Bongard, Andrew N. M...
134
Voted
FOIKS
2008
Springer
15 years 8 months ago
Cost-minimising strategies for data labelling : optimal stopping and active learning
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
Christos Dimitrakakis, Christian Savu-Krohn