NASA Scientific and Technical Aerospace Reports

A common obstacle preventing the rapid deployment of supervised machine learning algorithms is the lack of labeled
training data. This is particularly expensive to obtain for structured prediction tasks, where each training instance may have
multiple, interacting labels, all of which must be correctly annotated for the instance to be of use to the learner. Traditional
active learning addresses this problem by optimizing the order in which the examples are labeled to increase learning
efficiency. However, this approach does not consider the difficulty of labeling each example, which can vary widely in
structured prediction tasks. For example, the labeling predicted by a partially trained system may be easier to correct for some
instances than for others. We propose a new active learning paradigm which reduces not only how many instances the
annotator must label, but also how difficult each instance is to annotate. The system also leverages information from partially
correct predictions to efficiently solicit annotations from the user. We validate this active learning framework in an interactive
information extraction system, reducing the total number of annotation actions by 22%.
DTIC
Machine Learning; Algorithms
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THEORETICAL MATHEMATICS
Includes algebra, functional analysis, geometry, topology, set theory, group theory and number theory.
20060000046 Helsinki Univ. of Technology, Helsinki, Finland
Quasihyperbolic Geodesics and Uniformity in Elementary Domains
Linden, Henri; Martio, Olli, Editor; [2005]; ISSN 239-6303; 55 pp.; In English
Report No.(s): Rept-146; Copyright; Avail.: Other Sources
This work considers another definition of uniform domains, originally stated by F. Gehring and B. Osgood in [GeOs]. This
alternative definition uses comparison between the quasihyperbolic and the distance ratio metrics and involves also a constant
A greater than or equal to 1 which in general is not the same as the constant c in the definition by Martio and Sarvas. This
alternative definition is better suited to the studies here and adhere to the uniformity concept of Definition 2.4 in this work.
The class of uniform domains is very wide, for instance it includes images of the unit ball B(sup n) under a quasiconformal
mapping of R(sup n) into itself. It is perhaps surprising that there are very few examples of domains for which the uniformity
constant A is known, and in this work we study some of the simplest cases. In most cases this task involves identifying the
geodesic segments of the given domain-in general a difficult problem. Often such precise analysis requires domain-specific
methods; however, some techniques used might be of interest also in more general situations.
Derived from text
Domains; Geodesic Lines; Ratios
20060001249 National Chung Hsing Univ., Taichung, Taiwan, Province of China
Power System Load Frequency Control by Genetic Fuzzy Gain Scheduling Controller
Juang, Chia-Feng; Lu, Chun-Feng; Journal of the Chinese Institute of Engineers; Vol. 28, No. 6; October 2005,
pp. 1013-1018; In English; See also 20060001246; Copyright; Avail.: Other Sources
A Genetic Algorithm (GA) based fuzzy gain scheduling approach for load frequency control is proposed in this paper. In
this approach, a fuzzy system is used to adaptively decide the integral or PI controller gain according to the area control errors
(ACE) and their changes. To reduce both the fuzzy system design effort and the number of fuzzy rules, the fuzzy system is
designed automatically by genetic algorithms. To improve the design performance, a new genetic algorithm using elitist
strategy combined with similarity measure on relatives between individuals is proposed. Based on similarity measure, mutated
reproduction is adopted to increase the chance for reaching better solutions and to avoid premature phenomena. Simulations
on a two-area interconnected power system with different kinds of perturbation are performed. The superiority of the proposed
method over existing ones is verified from simulations and comparisons.
Author
Frequency Control; Genetic Algorithms
20060001620 Massachusetts Inst. of Tech., Lexington, MA, USA
Some Applications of Mathematical Morphology to Range Imagery
Esselman, T. R.; Verly, J. G.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87);
Volume 1; 1987, pp. 7.10.01 - 7.10.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
Although little known, mathematical morphology (MM) offers great potential in the areas of image enhancement, feature
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