88,704 research outputs found
Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction
Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659
Asymptotic Optimality Theory For Decentralized Sequential Multihypothesis Testing Problems
The Bayesian formulation of sequentially testing hypotheses is
studied in the context of a decentralized sensor network system. In such a
system, local sensors observe raw observations and send quantized sensor
messages to a fusion center which makes a final decision when stopping taking
observations. Asymptotically optimal decentralized sequential tests are
developed from a class of "two-stage" tests that allows the sensor network
system to make a preliminary decision in the first stage and then optimize each
local sensor quantizer accordingly in the second stage. It is shown that the
optimal local quantizer at each local sensor in the second stage can be defined
as a maximin quantizer which turns out to be a randomization of at most
unambiguous likelihood quantizers (ULQ). We first present in detail our results
for the system with a single sensor and binary sensor messages, and then extend
to more general cases involving any finite alphabet sensor messages, multiple
sensors, or composite hypotheses.Comment: 14 pages, 1 figure, submitted to IEEE Trans. Inf. Theor
Keep Ballots Secret: On the Futility of Social Learning in Decision Making by Voting
We show that social learning is not useful in a model of team binary decision
making by voting, where each vote carries equal weight. Specifically, we
consider Bayesian binary hypothesis testing where agents have any
conditionally-independent observation distribution and their local decisions
are fused by any L-out-of-N fusion rule. The agents make local decisions
sequentially, with each allowed to use its own private signal and all precedent
local decisions. Though social learning generally occurs in that precedent
local decisions affect an agent's belief, optimal team performance is obtained
when all precedent local decisions are ignored. Thus, social learning is
futile, and secret ballots are optimal. This contrasts with typical studies of
social learning because we include a fusion center rather than concentrating on
the performance of the latest-acting agents
Controlled Sensing for Multihypothesis Testing
The problem of multiple hypothesis testing with observation control is
considered in both fixed sample size and sequential settings. In the fixed
sample size setting, for binary hypothesis testing, the optimal exponent for
the maximal error probability corresponds to the maximum Chernoff information
over the choice of controls, and a pure stationary open-loop control policy is
asymptotically optimal within the larger class of all causal control policies.
For multihypothesis testing in the fixed sample size setting, lower and upper
bounds on the optimal error exponent are derived. It is also shown through an
example with three hypotheses that the optimal causal control policy can be
strictly better than the optimal open-loop control policy. In the sequential
setting, a test based on earlier work by Chernoff for binary hypothesis
testing, is shown to be first-order asymptotically optimal for multihypothesis
testing in a strong sense, using the notion of decision making risk in place of
the overall probability of error. Another test is also designed to meet hard
risk constrains while retaining asymptotic optimality. The role of past
information and randomization in designing optimal control policies is
discussed.Comment: To appear in the Transactions on Automatic Contro
Distributed Hypothesis Testing, Attention Shifts and Transmitter Dynatmics During the Self-Organization of Brain Recognition Codes
BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088
- β¦