Models of Incremental Concept Formation

88 - 06 Models of Incremental Concept Formation

IGMN: An Incremental Gaussian Mixture Network that Learns Instantaneously from Data Flows

This works proposes IGMN (standing for Incremental Gaussian Mixture Network), a new connectionist approach for incremental concept formation and robotic tasks. It is inspired on recent theories about the brain, specially the Memory-Prediction Framework and the Constructivist Artificial Intelligence, which endows it with some unique features that are not present in most ANN models such as MLP an...

Mining Data in Multi-perspectives

Knowledge discover in Data bases (KDD) concerns the development of models which allow the representation and organization of the knowledge hidden in data. Machine learning systems are an alternative to automate the KDD process. In particular, incremental concept formation systems construct hierarchical abstract representations from observations (non-classified description of specific entities, ...

Constructive Induction by Incremental Concept Formation

This paper describes a framework that generates constructive induction schemes for the concept formation system COBWEB. The basis of this framework|context-dependent bias of multi-valued properties|provides a way for allowing COBWEB to deal with continuous and hierarchical property types as a special case of constructive induction. The constructive induction scheme does not introduce learning b...

Incremental Concept Formation Algorithms Based on Galois (Concept) Lattices

The Galois (or concept) lattice produced from a binary relation has been proved useful for many applications. Building the Galois lattice can be considered as a conceptual clustering method since it results in a concept hierarchy. This article presents incremental algorithms for updating the Galois lattice and corresponding graph, resulting in an incremental concept formation method. Different ...

A New High-order Takagi-Sugeno Fuzzy Model Based on Deformed Linear Models

Amongst possible choices for identifying complicated processes for prediction, simulation, and approximation applications, high-order Takagi-Sugeno (TS) fuzzy models are fitting tools. Although they can construct models with rather high complexity, they are not as interpretable as first-order TS fuzzy models. In this paper, we first propose to use Deformed Linear Models (DLMs) in consequence pa...