Add to ORKGWe solve the problem of concept learning using a semi-tensor product method. All possible hypotheses are expressed under the framework of a semi-tensor product. An algorithm is raised to derive the version space. In some cases, the new approach improves the efficiency compared to the previous approach. doi:10.1017/S144618111600031
This thesis describes an exploration of methods involved in learning flexible concepts that is an im...
Our project has two threads: (1) building computational models of how people learn and structure sem...
Concept learning is about distilling interpretable rules and concepts from data, a prelude to more a...
Humans learn new concepts extremely fast. One or two examples of a new concept are often sufficient ...
AbstractThe iterated version space algorithm (IVSA) has been designed and implemented to learn disju...
[[abstract]]Applies the technique of parallel processing to concept learning. A parallel version-spa...
This paper investigates a general framework tor learning concepts that allows to generate accurate a...
W: Proceedings of the European Conference on Artificial Intelligence 11/5, Orsay, France, 1982, page...
Concept learning from examples in first-order languages has been widely studied recently. Specifical...
[[abstract]]Learning general concepts from a set of training instances has become increasingly impor...
In spite of the importance of representation in learning, little progress has been made toward under...
The yield optimization data analytic process is an iterative search process. Each iterationcomprises...
Most of the existing learning algorithms take vectors as their input data. A function is then learne...
Many authors have emphasized the role that concepts play as basic building blocks of cognition. This...
We previously introduced an exemplar model, named GCM-ISW, that exploits a highly flexible weighting...
This thesis describes an exploration of methods involved in learning flexible concepts that is an im...
Our project has two threads: (1) building computational models of how people learn and structure sem...
Concept learning is about distilling interpretable rules and concepts from data, a prelude to more a...
Humans learn new concepts extremely fast. One or two examples of a new concept are often sufficient ...
AbstractThe iterated version space algorithm (IVSA) has been designed and implemented to learn disju...
[[abstract]]Applies the technique of parallel processing to concept learning. A parallel version-spa...
This paper investigates a general framework tor learning concepts that allows to generate accurate a...
W: Proceedings of the European Conference on Artificial Intelligence 11/5, Orsay, France, 1982, page...
Concept learning from examples in first-order languages has been widely studied recently. Specifical...
[[abstract]]Learning general concepts from a set of training instances has become increasingly impor...
In spite of the importance of representation in learning, little progress has been made toward under...
The yield optimization data analytic process is an iterative search process. Each iterationcomprises...
Most of the existing learning algorithms take vectors as their input data. A function is then learne...
Many authors have emphasized the role that concepts play as basic building blocks of cognition. This...
We previously introduced an exemplar model, named GCM-ISW, that exploits a highly flexible weighting...
This thesis describes an exploration of methods involved in learning flexible concepts that is an im...
Our project has two threads: (1) building computational models of how people learn and structure sem...
Concept learning is about distilling interpretable rules and concepts from data, a prelude to more a...