Titlebook: Case-Based Reasoning Research and Development; 29th International C Antonio A. Sánchez-Ruiz,Michael W. Floyd Conference proceedings 2021 Sp

GLIB

Instance-Based Counterfactual Explanations for Time Series Classification,d modifying discriminative areas of the time series that underlie the classification. Quantitative and qualitative results from two comparative experiments indicate that Native Guide generates plausible, proximal, sparse and diverse explanations that are better than those produced by key benchmark counterfactual methods.

SEMI

隼鷹

陰謀

0302-9743 a, Spain, during September 13-16, 2021.?.The 21 papers presented in this volume were carefully reviewed and selected from 85 submissions. They deal with AI and related research focusing on comparison and integration of CBR with other AI methods such as deep learning architectures, reinforcement lear

hypotension

Advanced Information Systems Engineerings paper, we explain the first experiment to check our hypotheses and methodology. We have obtained promising results that explain the differences between users profiles and their perception of similarity between artworks and how to combine local similarity functions to be able to compute similarity measures reflecting users’ perception.

鋼筆記下懲罰

https://doi.org/10.1007/978-3-030-79382-1first introduces a new, generic structure for representing tasks and task plans. The paper, then, introduces a generic situation structure and a methodology of situation handling. The proposed structures support encoding all domain knowledge in . while avoiding hard-coding domain rules.

ANTH

Advanced Information Systems Engineeringdge and arriving at a complexity measure for evaluating dichotomous models. For all the three improvements identified, we discuss the results on real-world data sets and empirically analyse the effectiveness of the same.

Injunction

高深莫測(cè)

,Revisiting Fast and Slow Thinking in?Case-Based Reasoning,dge and arriving at a complexity measure for evaluating dichotomous models. For all the three improvements identified, we discuss the results on real-world data sets and empirically analyse the effectiveness of the same.

FEIGN

,Harmonizing Case Retrieval and?Adaptation with Alternating Optimization,ing neural network based similarity and adaptation, this study compares alternative optimization, independent learning, and learning by prioritizing adaptation for adaptation-guided retrieval. Results support that alternative optimization can help to balance both components and achieve good performance.