Authors:
Koichiro Yamauchi
1
and
Vanamala Narasimha Bhargav
2
Affiliations:
1
Chubu University, Japan
;
2
Indian Institute of Technology, India
Keyword(s):
Modal Regression, Kernel Distribution Estimator, Incremental Learning on a Budget, Kernel Machines, Projection Method.
Related
Ontology
Subjects/Areas/Topics:
Kernel Methods
;
Knowledge Acquisition and Representation
;
Pattern Recognition
;
Regression
;
Sparsity
;
Theory and Methods
Abstract:
The recent development of microcomputers enables the execution of complex software in small embedded systems. Artificial intelligence is one form of software to be embedded into such devices. However, almost all embedded systems still have restricted storage space. One of the authors has already proposed an incremental learning method for regression, which works under a fixed storage space; however, this method cannot support the multivalued functions that usually appear in real-world problems. One way to support the multivalued function is to use the model regression method with a kernel density estimator. However, this method assumes that all sample points are recorded as kernel centroids, which is not suitable for small embedded systems. In this paper, we propose a minimum modal regression method that reduces the number of kernels using a projection method. The conditions required to maintain accuracy are derived through theoretical analysis. The experimental results show that our
method reduces the number of kernels while maintaining a specified level of accuracy.
(More)