GECCO
2009
Springer
14 years 2 months ago
2009
Springer
Typical domains used in machine learning analyses only partially cover the complexity space, remaining a large proportion of problem difficulties that are not tested. Since the ac...
FPL
2009
Springer
14 years 2 months ago
2009
Springer
Restricted Boltzmann Machines (RBMs) — the building block for newly popular Deep Belief Networks (DBNs) — are a promising new tool for machine learning practitioners. However,...
ICADL
2010
Springer
14 years 2 months ago
2010
Springer
Maintaining and extending large thesauri is an important challenge facing digital libraries and IT businesses alike. In this paper we describe a method building on and extending ex...
AI
2010
Springer
14 years 2 months ago
2010
Springer
This paper presents a supervised machine learning approach for summarizing legal documents. A commercial system for the analysis and summarization of legal documents provided us wi...
GCB
2003
Springer
14 years 2 months ago
2003
Springer
: Classification and prediction of protein structure has been a central research theme in structural bioinformatics. Due to the imbalanced distribution of proteins over multi SCOP ...
ESSMAC
2003
Springer
14 years 2 months ago
2003
Springer
Abstract. We consider a linear system, such as an estimator or a controller, in which several signals are transmitted over wireless communication channels. With the coding and medi...
ESSMAC
2003
Springer
14 years 2 months ago
2003
Springer
We present a gestural interface for entering text on a mobile device via continuous movements, with control based on feedback from a probabilistic language model. Text is represent...
ESSMAC
2003
Springer
14 years 2 months ago
2003
Springer
ESSMAC
2003
Springer
14 years 2 months ago
2003
Springer
Kernel-based non-parametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those me...
ESSMAC
2003
Springer
14 years 2 months ago
2003
Springer
Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a...