Luc Lamontagne, Enric Plaza's Case-Based Reasoning Research and Development: 22nd PDF

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By Luc Lamontagne, Enric Plaza

ISBN-10: 3319112082

ISBN-13: 9783319112084

ISBN-10: 3319112090

ISBN-13: 9783319112091

This e-book constitutes the refereed court cases of the twenty first foreign convention on Case-Based Reasoning examine and improvement (ICCBR 2014) held in Cork, eire, in September 2014. The 35 revised complete papers provided have been rigorously reviewed and chosen from forty nine submissions. The shows conceal quite a lot of CBR subject matters of curiosity either to researchers and practitioners together with case retrieval and model, similarity review, case base upkeep, wisdom administration, recommender structures, multiagent structures, textual CBR, and purposes to healthcare and laptop games.

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Read or Download Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings PDF

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Extra resources for Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings

Example text

The problem is then to estimate this distribution or, equivalently, the parameter θ on the basis of the information available. This information consists of a set N (2) D = y (i) z (i) i=1 of preferences of the form y z between solutions. The basic assumption underlying nearest neighbor estimation is that the conditional probability distribution of the output given the input is (approximately) locally constant, that is, P(· | x0 ) ≈ P(· | x) for x close to x0 . Thus, if the preferences (2) are coming from problems x similar to x0 (namely from the nearest neighbors of x0 in the case base), then this assumption justifies considering D as a representative sample of Pθ (·) and, hence, estimating θ via maximum likelihood (ML) inference by θML = argmax Pθ (D) .

2 Bayesian Distance Learning Adopting the above representation of the distance measure ΔY , our choice model (4) is now given by P(y z) = SY (y, y ∗ ) SY (y, y ∗ ) + SY (z, y ∗ ) (8) with k SY (y, y ∗ ) = exp − γi · Δi (y, y ∗ ) i=1 (9) 24 A. Abdel-Aziz, M. Strickert, and E. H¨ ullermeier and γi = β · αi ≥ 0. Thus, learning γ = (γ1 , . . , γk ) means learning β and α simultaneously. In fact, these parameters can be recovered from γ as follows: β = γ1 + γ2 + . . + γk αi = γi /β For simplicity, suppose that γ = (γ1 , .

1 A Local-Global Representation of Distance We begin with a simplifying assumption on the structure of the distance measure ΔY , namely that it adheres to the local-global principle [2] and takes the form k ΔY (y, y ∗ ) = αi · Δi (y, y ∗ ) , (7) i=1 where Δ1 , . . , Δk are local distances pertaining to different properties of solutions, and α = (α1 , . . , the coefficients αi are non-negative and sum up to 1). We assume the Δi to be known, whereas the αi , which are modeling the importance of the local distances, are supposed to be unknown.

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Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings by Luc Lamontagne, Enric Plaza


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