Computational Intelligence - February 2014 - 15

Publication
Spotlight

Derong Liu, Chin-Teng Lin,
Garry Greenwood, Simon Lucas,
and Zhengyou Zhang

CIS Publication Spotlight

IEEE Transactions on Neural
Networks and Learning Systems

Transfer Ordinal Label Learning, by C.
W. Seah, I.W. Tsang, and Y.S. Ong,
IEEE Transactions on Neural Networks
and Learning Systems, Vol. 24, No. 11,
November 2013, pp. 1863-1876.
Digital Object Identifier: 10.1109/
TNNLS.2013.2268541
"Designing a classifier in the absence
of labeled data is becoming a common
encounter as the acquisition of informative labels is often difficult or expensive,
particularly on new uncharted target
domains.The feasibility of attaining a reliable classifier for the task of interest is
embarked by some in transfer learning,
where label information from relevant
source domains is considered for complimenting the design process. The core
challenge arising from such endeavors,
however, is the induction of source sample selection bias, such that the trained
classifier has the tendency of steering
toward the distribution of the source
domain. In addition, this bias is deemed
to become more severe on data involving
multiple classes. Considering this cue, our
interest in this paper is to address such a
challenge in the target domain, where
ordinal labeled data are unavailable. In
contrast to the previous works, a transfer
ordinal label learning paradigm is introduced to predict the ordinal labels of target unlabeled data by spanning the
Digital Object Identifier 10.1109/MCI.2013.2291678
Date of publication: 14 January 2014

feasible solution space with
ensemble of ordinal
classifiers from the multiple relevant source
domains. Specifically,
the maximum margin criterion is con© eyewire
sidered for the construction of the target classifier from an
ensemble of source ordinal classifiers.
Theoretical analysis and extensive empirical studies on real-world data sets are
presented to study the benefits of the
proposed method."
Adaptive Optimal Control of Unknown
Constrained-Input Systems Using Policy
Iteration and Neural Networks, by H.
Modares, F.L. Lewis, and M.B. Naghibi-Sistani, IEEE Transactions on Neural
Networks and Learning Systems, Vol. 24,
No. 10, October 2013, pp. 1513-1525.
Digital Object Identifier: 10.1109/
TNNLS.2013.2276571
"An online policy iteration (PI) algorithm is developed to learn the continuous-time optimal control solution for
unknown constrained-input systems. The
proposed PI algorithm is implemented
on an actor-critic structure where two
neural networks (NNs) are tuned online
and simultaneously to generate the optimal bounded control policy. The
requirement of complete knowledge of
the system dynamics is obviated by
employing a novel NN identifier in conjunction with the actor and critic NNs.
It is shown how the identifier weights
estimation error affects the convergence

of the critic NN. A novel
learning rule is developed to guarantee
that the identifier
weights converge
to small neighborhoods of their ideal
values exponentially fast.
To provide an easy-to-check
persistence of excitation condition, the experience replay technique is
used. That is, recorded past experiences
are used simultaneously with current
data for the adaptation of the identifier
weights. Stability of the whole system
consisting of the actor, critic, system state,
and system identifier is guaranteed while
all three networks undergo adaptation.
Convergence to a near-optimal control
law is also shown. The effectiveness of
the proposed method is illustrated with a
simulation example."
IEEE Transactions
on Fuzzy Systems

Clustering Spatiotemporal Data: An
Augmented Fuzzy C-Means, IEEE
Transactions on Fuzzy Systems, Vol. 21,
No. 5, October 2013, pp. 855-868.
Digital Object Identifier: 10.1109/
TFUZZ.2012.2233479
"In spatiotemporal data commonly
encountered in geographical systems, biomedical signals, and the like, each datum is
composed of features comprising a spatial
component and a temporal part. Clustering of data of this nature poses challenges,
especially in terms of a suitable treatment

February 2014 | Ieee ComputatIonal IntellIgenCe magazIne

Table of Contents for the Digital Edition of Computational Intelligence - February 2014