1,721,071 research outputs found
Devising novel performance measures for assessing the behavior of multilayer perceptrons trained on regression tasks
Full text linkThis methodological article is mainly aimed at establishing a bridge between classification and regression tasks, in a frame shaped by performance evaluation. More specifically, a general procedure for calculating performance measures is proposed, which can be applied to both classification and regression models. To this end, a notable change in the policy used to evaluate the confusion matrix is made, with the goal of reporting information about regression performance therein. This policy, called generalized token sharing, allows to a) assess models trained on both classification and regression tasks, b) evaluate the importance of input features, and c) inspect the behavior of multilayer perceptrons by looking at their hidden layers. The occurrence of success and failure patterns at the hidden layers of multilayer perceptrons trained and tested on selected regression problems, together with the effectiveness of layer-wise training, is also discussed
A MultiAgent System for Retrieving Bioinformatics Publications from Web Sources
No full textDue to the enormous amount of information available on the Internet, extracting and classifying it has become one of the most important tasks. This principle is valid also while searching for scientific publications. This paper describes a system able to retrieve scientific publications from the Web throughout a text categorization process. To this end, a generic multiagent architecture has been customized according to the requirements imposed by the specific task. Experiments have been performed on publications extracted from BMC Bioinformatics and PubMed digital archives
Automatic Extraction and Classification of Bioinformatics Publications through a MultiAgent System
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PACMAS: A Personalized, Adaptive, and Cooperative MultiAgent System Architecture
Full text linkIn this paper, a generic architecture, designed to
support the implementation of applications aimed at managing
information among different and heterogeneous sources,
is presented. Information is filtered and organized according
to personal interests explicitly stated by the user. User pro-
files are improved and refined throughout time by suitable
adaptation techniques. The overall architecture has been called
PACMAS, being a support for implementing Personalized, Adaptive,
and Cooperative MultiAgent Systems. PACMAS agents are
autonomous and flexible, and can be made personal, adaptive and
cooperative, depending on the given application. The peculiarities
of the architecture are highlighted by illustrating three relevant
case studies focused on giving a support to undergraduate and
graduate students, on predicting protein secondary structure, and
on classifying newspaper articles, respectively
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