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IJHIS
2008
84views more  IJHIS 2008»
10 years 2 months ago
Selective generation of training examples in active meta-learning
Meta-Learning has been successfully applied to acquire knowledge used to support the selection of learning algorithms. Each training example in Meta-Learning (i.e. each meta-exampl...
Ricardo Bastos Cavalcante Prudêncio, Teresa ...
IJCAI
1989
10 years 3 months ago
Coping With Uncertainty in Map Learning
In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refe...
Kenneth Basye, Thomas Dean, Jeffrey Scott Vitter
NIPS
2007
10 years 4 months ago
The Tradeoffs of Large Scale Learning
This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for...
Léon Bottou, Olivier Bousquet
HIS
2008
10 years 4 months ago
Neural Plasticity and Minimal Topologies for Reward-Based Learning
Artificial Neural Networks for online learning problems are often implemented with synaptic plasticity to achieve adaptive behaviour. A common problem is that the overall learning...
Andrea Soltoggio
AAAI
2010
10 years 4 months ago
Constraint Programming for Data Mining and Machine Learning
Machine learning and data mining have become aware that using constraints when learning patterns and rules can be very useful. To this end, a large number of special purpose syste...
Luc De Raedt, Tias Guns, Siegfried Nijssen
ICAPR
2005
Springer
10 years 8 months ago
Multi-view EM Algorithm for Finite Mixture Models
In this paper, Multi-View Expectation and Maximization algorithm for finite mixture models is proposed by us to handle realworld learning problems which have natural feature split...
Xing Yi, Yunpeng Xu, Changshui Zhang
ICASSP
2008
IEEE
10 years 9 months ago
Learning to satisfy
This paper investigates a class of learning problems called learning satisfiability (LSAT) problems, where the goal is to learn a set in the input (feature) space that satisfies...
Frederic Thouin, Mark Coates, Brian Eriksson, Robe...
EPIA
2009
Springer
10 years 9 months ago
An ILP System for Learning Head Output Connected Predicates
Inductive Logic Programming (ILP) [1] systems are general purpose learners that have had significant success on solving a number of relational problems, particularly from the biol...
José Carlos Almeida Santos, Alireza Tamaddo...
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