Lazy learning is a general learning principle in which models are constructed from a database of cases on an ‘as needed’ basis. Methods include instance-based approaches like
nearest neighbour and case based reasoning. Another learning paradigm with a lot of commonality is competitive learning, in which populations of units or high-order modules compete for attention to adapt or respond (produce system output). This work reviews these two general learning paradigms and proposes the clonal selection approach as possessing aspects of laziness whilst being strongly competitive
We approach the difficult task of analyzing the complex behavior of even the simplest learning class...
Proceeding of: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 20...
The minimal clonal selection provides a reduced realisation of the principles of the clonal selectio...
In this paper, we propose a lazy learning strategy for building classification learning models. Inst...
Lazy learning methods have been used to deal with problems in which the learning examples are not ev...
This report has the purpose of describing several algorithms from the literature all related to comp...
Rumelhart & Zipser's (1986) competitive learning algorithm is an account of unsupcrvised le...
Learning in the context of constraint solving is a technique by which previously unknown constraints...
Learning in the context of constraint solving is a technique by which previously unknown constraints...
Clonal selection has been a dominant theme in many immune-inspired algorithms applied to machine lea...
AbstractClonal selection has been a dominant theme in many immune-inspired algorithms applied to mac...
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction inte...
This paper summarizes recent research on competition-based learning procedures performed by the Navy...
The traditional approach to supervised learning is global modeling which describes the relationship ...
Determining the conditions for which a given learning algorithm is appropriate is an open problem in...
We approach the difficult task of analyzing the complex behavior of even the simplest learning class...
Proceeding of: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 20...
The minimal clonal selection provides a reduced realisation of the principles of the clonal selectio...
In this paper, we propose a lazy learning strategy for building classification learning models. Inst...
Lazy learning methods have been used to deal with problems in which the learning examples are not ev...
This report has the purpose of describing several algorithms from the literature all related to comp...
Rumelhart & Zipser's (1986) competitive learning algorithm is an account of unsupcrvised le...
Learning in the context of constraint solving is a technique by which previously unknown constraints...
Learning in the context of constraint solving is a technique by which previously unknown constraints...
Clonal selection has been a dominant theme in many immune-inspired algorithms applied to machine lea...
AbstractClonal selection has been a dominant theme in many immune-inspired algorithms applied to mac...
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction inte...
This paper summarizes recent research on competition-based learning procedures performed by the Navy...
The traditional approach to supervised learning is global modeling which describes the relationship ...
Determining the conditions for which a given learning algorithm is appropriate is an open problem in...
We approach the difficult task of analyzing the complex behavior of even the simplest learning class...
Proceeding of: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 20...
The minimal clonal selection provides a reduced realisation of the principles of the clonal selectio...