189,068 research outputs found
Query Learning with Exponential Query Costs
In query learning, the goal is to identify an unknown object while minimizing
the number of "yes" or "no" questions (queries) posed about that object. A
well-studied algorithm for query learning is known as generalized binary search
(GBS). We show that GBS is a greedy algorithm to optimize the expected number
of queries needed to identify the unknown object. We also generalize GBS in two
ways. First, we consider the case where the cost of querying grows
exponentially in the number of queries and the goal is to minimize the expected
exponential cost. Then, we consider the case where the objects are partitioned
into groups, and the objective is to identify only the group to which the
object belongs. We derive algorithms to address these issues in a common,
information-theoretic framework. In particular, we present an exact formula for
the objective function in each case involving Shannon or Renyi entropy, and
develop a greedy algorithm for minimizing it. Our algorithms are demonstrated
on two applications of query learning, active learning and emergency response.Comment: 15 page
ARTMAP-FTR: A Neural Network For Fusion Target Recognition, With Application To Sonar Classification
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657
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