New specifics for a hierarchial estimator meta-algorithm

Authors:
Stanisław Brodowski;Andrzej Bielecki
Affiliations:
Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland;Institute of Computer Science, Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Year:
2012

Citing 13
Cited 0

The Strength of Weak Learnability

Machine Learning
Hierarchical mixtures of experts and the EM algorithm

Neural Computation
A comparative study of self-organizing clustering algorithms dignet and ART2

Neural Networks
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Principles of data mining

Principles of data mining
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
Hierarchical classifier with overlapping class groups

Expert Systems with Applications: An International Journal
AdaBoost is Consistent

The Journal of Machine Learning Research
Hierarchical Estimator

Expert Systems with Applications: An International Journal
A validity criterion for fuzzy clustering

ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
On cluster validity for the fuzzy c-means model

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Hierarchical Estimator is a meta-algorithm presented in [1] concerned with learning a nonlinear relation between two vector variables from training data, which is one of the core tasks of machine learning, primarily for the purpose of prediction. It arranges many simple function approximators into a tree-like structure in order to achieve a solution with a low error. This paper presents a new version of specifics for that meta-algorithm --- a so called training set division and a competence function creation method. The included experimental results show improvement over the methods described in [1]. A short recollection of Hierarchical Estimator is also included.