By Luc Lamontagne, Enric Plaza
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 conscientiously reviewed and chosen from forty nine submissions. The displays conceal quite a lot of CBR themes of curiosity either to researchers and practitioners together with case retrieval and version, similarity evaluate, case base upkeep, wisdom administration, recommender structures, multiagent platforms, textual CBR, and functions to healthcare and laptop games.
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
Aha Algorithm 1. S do // Compute value for parameter s with the highest weighted vote Solution[s] = maxParamValue(V otes[s]/SumW eights[s]); return Solution; calculating a Student t-statistic1 for its Eﬃciency relative to some set of cases C as follows: ce − C e score(c, C) = , (2) s where ce is the eﬃciency of case c, C e is the mean eﬃciency of all cases in C, and s is the sample deviation of C. For each case c ∈ L , we compute its score(c,L ). For each subset of cases Lp ⊂ L with problem p, we identify its n% most Eﬃcient cases and compute their mean, denoted by mean(L , p, n).
To simulate this scenario, we made use of the red wine data set from the UCI machine learning repository . This data describes 1599 red wines in terms of diﬀerent chemical properties; here, we only used three of them, namely sulphates (y1 ), pH (y2 ), and total sulfur dioxide (y3 ), which were found to have the strongest inﬂuence on preference . We randomly extracted 500 wines to constitute the wines in the cellar, while the remaining 1500 were used as queries. Thus, a query is a wine that is thought of as the ideal solution of a customer (in this example, problem space and solution space therefore have the same structure).
Appropriate metrics are also essential for the performance of distance-based methods such as nearest neighbor estimation, which are used for classiﬁcation, regression, and related problems. Metric learning has therefore been studied quite intensively in machine learning and pattern recognition. While Mahalanobis distance metric learning has received speciﬁc attention in this regard, more involved problems such as nonlinear metric learning, local metric learning, semi-supervised metric learning, and metric learning for structured data have been tackled more recently.