Using Case-Based Reasoning in Interpreting Unsupervised Inductive Learning Results
نویسندگان
چکیده
The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts concept hierarchies from unlabelled data, based on a representation combining the classical, prototype and exemplar views on concepts. The interpretive process is considered as an intrinsic part in OSHAM and is carried out by a combination of case-based reasoning with matching approaches in inductive learning. An experimental comparative study of some learning methods in terms of knowledge description and prediction is given.
منابع مشابه
Using Case-based Reasoning in Interpreting Unsupervised Inductive Learning Results 3
The objective of this work is to interpret induc-tive results obtained by the unsupervised learning method OSHAM. We brie BLOCKINy introduce the learning process of OSHAM, that extracts concept hierarchies from unlabelled data, based on a representation combining the classical, prototype and exemplar views on concepts. The in-terpretive process is considered as an intrinsic part in OSHAM and is...
متن کاملInductive Hypothesis Validation and Bias Selection in Unsupervised Learning
This paper approaches the importance of bias selection in the context of validating Knowledge Bases (KB) obtained by inductive learning systems. We propose a framework for automatic validation of induced KBs based on the capability of shifting the bias in the inductive learning system. We claim that this framework is useful not only when the system has to validate its own results, but also when...
متن کاملImproving Agent Performance for Multi-Resource Negotiation Using Learning Automata and Case-Based Reasoning
In electronic commerce markets, agents often should acquire multiple resources to fulfil a high-level task. In order to attain such resources they need to compete with each other. In multi-agent environments, in which competition is involved, negotiation would be an interaction between agents in order to reach an agreement on resource allocation and to be coordinated with each other. In recent ...
متن کاملUsing Bayesian Classification for Aq-based Learning with Constructive Induction
To obtain potentially interesting patterns and relations from large, distributed, heterogeneous databases, it is essential to employ an intelligent and automated KDD (Knowledge Discovery in Databases) process. One of the most important methodologies is an integration of diverse learning strategies that cooperatively performs a variety of techniques and achieves high quality knowledge. AqBC is a...
متن کاملOn Analytical and Similarity-Based Classification
This paper is concerned with knowledge representation issues in machine learning. In particular, it presents a representation language that supports a hybrid analytical and similarity-based classification scheme. Analytical classification is produced using a KL-ONE-like term-subsumption strategy, while similarity-based classification is driven by generalizations induced from a training set by a...
متن کامل