Memory for instance information in concept learning

Learning Concept Mappings from Instance Similarity

Finding mappings between compatible ontologies is an important but difficult open problem. Instance-based methods for solving this problem have the advantage of focusing on the most active parts of the ontologies and reflect concept semantics as they are actually being used. However such methods have not at present been widely investigated in ontology mapping, compared to linguistic and structu...

Multiple-Instance Learning for Music Information Retrieval

•Multiple-instance learning (MIL) algorithms train classifiers from lightly supervised data – collections of instances, called bags, are labeled rather than the instances themselves – algorithms can classify bags or instances, we focus on instances •Motivation for applying MIL to MIR: – propagate metadata between granularities: artist, album, track, 10-second clip – e.g. train clip classifiers ...

Incremental Learning with Partial Instance Memory

Learning Instance Weights in Multi-Instance Learning

Multi-instance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification. This thesis investigates the case where each instance has a weight value determining the level of influ...

Incremental Rule Learning with Partial Instance Memory for Changing Concepts

Learning concepts that change over time is important for a variety of applications in which an intelligent system must acquire and use a behavioral profile. Computer intrusion detection, calendar scheduling, and intelligent user interfaces are three examples. An interesting class of methods for learning such concepts consists of algorithms that maintain a portion of previously encountered examp...