8,693 research outputs found
Duration and Interval Hidden Markov Model for Sequential Data Analysis
Analysis of sequential event data has been recognized as one of the essential
tools in data modeling and analysis field. In this paper, after the examination
of its technical requirements and issues to model complex but practical
situation, we propose a new sequential data model, dubbed Duration and Interval
Hidden Markov Model (DI-HMM), that efficiently represents "state duration" and
"state interval" of data events. This has significant implications to play an
important role in representing practical time-series sequential data. This
eventually provides an efficient and flexible sequential data retrieval.
Numerical experiments on synthetic and real data demonstrate the efficiency and
accuracy of the proposed DI-HMM
Handwritten digit recognition by bio-inspired hierarchical networks
The human brain processes information showing learning and prediction
abilities but the underlying neuronal mechanisms still remain unknown.
Recently, many studies prove that neuronal networks are able of both
generalizations and associations of sensory inputs. In this paper, following a
set of neurophysiological evidences, we propose a learning framework with a
strong biological plausibility that mimics prominent functions of cortical
circuitries. We developed the Inductive Conceptual Network (ICN), that is a
hierarchical bio-inspired network, able to learn invariant patterns by
Variable-order Markov Models implemented in its nodes. The outputs of the
top-most node of ICN hierarchy, representing the highest input generalization,
allow for automatic classification of inputs. We found that the ICN clusterized
MNIST images with an error of 5.73% and USPS images with an error of 12.56%
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