IWLCS
2005
Springer
14 years 5 months ago
2005
Springer
IWLCS
2005
Springer
14 years 5 months ago
2005
Springer
Aiming to clarify the convergence or divergence conditions for Learning Classifier System (LCS), this paper explores: (1) an extreme condition where the reinforcement process of ...
IWANN
2005
Springer
14 years 5 months ago
2005
Springer
Abstract. In Machine Learning, ensembles are combination of classifiers. Their objective is to improve the accuracy. In previous works, we have presented a method for the generati...
ILP
2005
Springer
14 years 5 months ago
2005
Springer
In this paper we survey work being conducted at Imperial College on the use of machine learning to build Systems Biology models of the effects of toxins on biochemical pathways. Se...
ILP
2005
Springer
14 years 5 months ago
2005
Springer
Online learning algorithms such as Winnow have received much attention in Machine Learning. Their performance degrades only logarithmically with the input dimension, making them us...
IDEAL
2005
Springer
14 years 5 months ago
2005
Springer
Prediction of peroxisomal matrix proteins generally depends on the presence of one of two distinct motifs at the end of the amino acid sequence. PTS1 peroxisomal proteins have a we...
IDA
2005
Springer
14 years 5 months ago
2005
Springer
Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned w...
ICMLC
2005
Springer
14 years 5 months ago
2005
Springer
This paper presents a novel approach for adaptive online multi-stroke sketch recognition based on Hidden Markov Model (HMM). The method views the drawing sketch as the result of a ...
ICMLC
2005
Springer
14 years 5 months ago
2005
Springer
A novel method of Interactive Evolutionary Computation (IEC) for the design of microelectromechanical systems (MEMS) is presented. As the main limitation of IEC is human fatigue, a...
ICMLC
2005
Springer
14 years 5 months ago
2005
Springer
In this paper we present a new method, time-striding hidden Markov model (TSHMM), to learn from long-term motion for atomic behaviors and the statistical dependencies among them. T...