N. K. Malakar
Maximum Joint Entropy and Information-Based Collaboration of Automated Learning Machines.
Wed, 11/16/2011 - 15:01 — SomNambulAuthors: N. K. Malakar, K. H. Knuth, D. J. Lary
Subjects: Machine Learning
AbstractWe are working to develop automated intelligent agents, which can act and
react as learning machines with minimal human intervention. To accomplish this,
an intelligent agent is viewed as a question-asking machine, which is designed
by coupling the processes of inference and inquiry to form a model-based
learning unit. In order to select maximally-informative queries, the
intelligent agent needs to be able to compute the relevance of a question.Entropy-Based Search Algorithm for Experimental Design.
Tue, 08/31/2010 - 15:01 — SomNambulAuthors: N. K. Malakar, K. H. Knuth
Subjects: Machine Learning
AbstractThe scientific method relies on the iterated processes of inference and
inquiry. The inference phase consists of selecting the most probable models
based on the available data; whereas the inquiry phase consists of using what
is known about the models to select the most relevant experiment. Optimizing
inquiry involves searching the parameterized space of experiments to select the
experiment that promises, on average, to be maximally informative.
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