Download e-book for kindle: Progress in Artificial Intelligence: 13th Portuguese by Matthias Knorr, José Júlio Alferes, Pascal Hitzler (auth.),

By Matthias Knorr, José Júlio Alferes, Pascal Hitzler (auth.), José Neves, Manuel Filipe Santos, José Manuel Machado (eds.)

ISBN-10: 3540770003

ISBN-13: 9783540770008

This publication constitutes the refereed court cases of the thirteenth Portuguese convention on man made Intelligence, EPIA 2007, held in Guimarães, Portugal, in December 2007 as 11 built-in workshops.

The fifty eight revised complete papers provided have been rigorously reviewed and chosen from a complete of 210 submissions. in line with the 11 constituting workshops, the papers are prepared in topical sections on normal synthetic intelligence (GAIW 2007), AI purposes for sustainable transportation structures (AIASTS 2007), synthetic lifestyles and evolutionary algorithms (ALEA 2007), ambient intelligence applied sciences and functions (AMITA 2007), development and utilizing ontologies for the semantic internet (BAOSW 2007), enterprise intelligence (BI 2007), computational equipment in bioinformatics and platforms biology (CMBSB 2007), clever robotics (IROBOT 2007), multi-agent structures: conception and functions (MASTA 2007), seek recommendations for constraint delight (STCS 2007), and textual content mining and functions (TEMA 2007).

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Read Online or Download Progress in Artificial Intelligence: 13th Portuguese Conference on Aritficial Intelligence, EPIA 2007, Workshops: GAIW, AIASTS, ALEA, AMITA, BAOSW, BI, CMBSB, IROBOT, MASTA, STCS, and TEMA, Guimarães, Portugal, December 3-7, 2007. Proceedings PDF

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Additional resources for Progress in Artificial Intelligence: 13th Portuguese Conference on Aritficial Intelligence, EPIA 2007, Workshops: GAIW, AIASTS, ALEA, AMITA, BAOSW, BI, CMBSB, IROBOT, MASTA, STCS, and TEMA, Guimarães, Portugal, December 3-7, 2007. Proceedings

Example text

Quasi-online learning is more efficient than the BPTT algorithm [8]. According to this, the RTRL algorithm is used in this paper. Therefore we make use of the developed formulas of the layered digital dynamic network. The interested reader is referred to [5]-[8] for further details. 4 Architecture Selection Pruning algorithms [1]–[4] have initially been designed for static neural networks to obtain good generalization. 2 we do not have overtraining problems. This paper is focused on the question: Is it possible to find the optimal structure of a nonlinear dynamic system?

The simple linear system identification example is chosen to show the weight reduction ability. The input weight of the final GDNN is very small to get an almost linear behavior of the model in the system’s operating range. In addition to that the weights of the GDNN model correspond very closely to the coefficients of the according difference equation. If the GDNN is too small, the error increases with the pruning process. This is shown in the nonlinear plant identification example. In that case the user has to know the quality demand of the final GDNN model.

Also note that rules 11 and 12 cause a direct contradiction with rule 8 for good wines. The semantics based on the causal rejection of rules deals with this issue. Also worth noting is that, for each recommendation, there is an explanation based on user rules, on owner rules, or on the initial recommendation (as is the case of product d7 ). This and other properties are explored in the next Section. 5 Properties The use of formal semantics enables a rigorous account of its behaviour. In this Section we discuss some properties of the Stable Recommendation Semantics.

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Progress in Artificial Intelligence: 13th Portuguese Conference on Aritficial Intelligence, EPIA 2007, Workshops: GAIW, AIASTS, ALEA, AMITA, BAOSW, BI, CMBSB, IROBOT, MASTA, STCS, and TEMA, Guimarães, Portugal, December 3-7, 2007. Proceedings by Matthias Knorr, José Júlio Alferes, Pascal Hitzler (auth.), José Neves, Manuel Filipe Santos, José Manuel Machado (eds.)


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