Learning a Local Similarity Metric for Case-Based Reasoning

A Minimum Risk Metric for Nearest Neighbor Classification

nale. Retrieval in a prototype-based case library: A case study in diabetes therapy revision. CH97] C. Cardie and N. Howe. Improving minority class prediction using case-speciic feature weight. CS93] Scott Cost and Steven Salzberg. A weighted nearest neighbor algorithm for learning with symbolic features. DP97] Pedro Domingos and Michael Pazzani. On the optimality of the simple bayesian clas-si...

Case-Based Reasoning for Explaining Probabilistic Machine Learning

This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As ba...

ROBUSTNESS OF THE TRIPLE IMPLICATION INFERENCE METHOD BASED ON THE WEIGHTED LOGIC METRIC

This paper focuses on the robustness problem of full implication triple implication inference method for fuzzy reasoning. First of all, based on strong regular implication, the weighted logic metric for measuring distance between two fuzzy sets is proposed. Besides, under this metric, some robustness results of the triple implication method are obtained, which demonstrates that the triple impli...

Learning an Asymmetric and Anisotropic Similarity Metric for Case-Based Reasoning

An Effective Approach for Robust Metric Learning in the Presence of Label Noise

Many algorithms in machine learning, pattern recognition, and data mining are based on a similarity/distance measure. For example, the kNN classifier and clustering algorithms such as k-means require a similarity/distance function. Also, in Content-Based Information Retrieval (CBIR) systems, we need to rank the retrieved objects based on the similarity to the query. As generic measures such as ...

Composite Kernel Optimization in Semi-Supervised Metric

Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...