By Luc Lamontagne, Enric Plaza
This publication constitutes the refereed lawsuits of the twenty first overseas convention on Case-Based Reasoning study and improvement (ICCBR 2014) held in Cork, eire, in September 2014. The 35 revised complete papers awarded have been rigorously reviewed and chosen from forty nine submissions. The shows hide quite a lot of CBR subject matters of curiosity either to researchers and practitioners together with case retrieval and variation, similarity review, case base upkeep, wisdom administration, recommender platforms, multiagent structures, textual CBR, and purposes to healthcare and computing device games.
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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
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).
The observed preference (either y z or z y) was then generated with our model (4) using the true y ∗ , while distance learning was done using this model with the estimate y • . The eﬀect of learning from noisy examples can be seen in Figure 2 (right), where we again show boxplots for the mean value estimator (14) based on 100 repetitions of the learning procedure with N = 100. As can be seen, the noise level σ does not seem to have a strong inﬂuence on the variance of the estimation. 5. 5 in our case) is plausible: The more y • deviates from y ∗ , the more noisy the examples will be for our distance learner—in the limit, they will become purely random, and on average, all local distances Δi will seemingly have the same inﬂuence then.
H¨ ullermeier Similarity learning in CBR has almost exclusively focused on learning similarity in the problem space. This is also true for the work of Stahl [12,16,13,15], which nevertheless shares a number of commonalities with our approach. In particular, he also considers the learning of weights in a linear combination of local similarity functions [12,14], albeit based on diﬀerent types of training information and using other learning techniques. Our own previous work  is related, too, as it learns from qualitative feedback in the form of preferences.