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 Programme
Overview
Detailed
Programme
| Machine Learning
and Heuristics II (MLHII) |
CHAIR: RUDOLF ALBRECHT
Time: Monday 21st
March, 14h20-16h00
| MLHII-1 |
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| Title: |
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Visualization of Meta-Reasoning
in Multi-Agent Systems |
| Author(s): |
D. Rehor,
J. Tozicka,
P. Slavik |
| Abstract: |
This paper describes the advances of our research
on visualization of multi-agent systems (MAS) for purposes
of analysis, monitoring and debugging. MAS are getting more
complex and widely used, such analysis tools are highly beneficial
in order to achieve better understanding of agent behaviour.
Our solution is based on our originally offline visualization
tools suite, which now uses a new realtime data acquisition
framework. In this case we have focused on agent meta-reasoning
in a MAS for planning of humanitarian relief operations. Previous
tools were unable to deal with complex characteristics of
these simulations, thus we have made some progress to present
maximum of important information for realtime monitoring.
This paper describes this progress, declares conditions and
proposes visualization methods, which fulfil them
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| MLHII-2 |
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| Title: |
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Intelligent Agent inspired Genetic
Algorithm |
| Author(s): |
C. Wu,
Y. Lian,
H. Lee,
C. Lu |
| Abstract: |
An intelligent agent-inspired genetic algorithm
(IAGA) is proposed. Analogous to the intelligent agent, each
individual in IAGA has its own properties, including crossover
probability, mutation probability, etc. Numerical simulations
demonstrate that, compared with the classical GA where all
individuals in a population share the same crossover and mutation
probabilities, the proposed algorithm is more efficient and
effective. |
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| MLHII-3 |
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| Title: |
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Combining Lazy Learning, Racing
and Subsampling for Effective Feature Selection |
| Author(s): |
Gianluca Bontempi,
Mauro Birattari,
Patrick E. Meyer |
| Abstract: |
This paper presents a wrapper method for feature
selection that combines Lazy Learning, racing and sub-sampling
techniques. Lazy Learning (LL) is a local learning technique
that, once a query is received, extracts a prediction by locally
interpolating the neighboring examples of the query which
are considered relevant according to a distance measure. Local
learning techniques are often criticized for their limitations
in dealing with problems with high number of features and
large samples. Similarly wrapper methods are considered prohibitive
for large number of features, due to the high cost of the
evaluation step. The paper aims to show that a wrapper feature
selection method based on LL can take advantage of two effective
strategies: racing and sub-sampling. While the idea of racing
was already proposed by Maron and Moore, this paper goes a
step further by (i) proposing a multiple testing technique
for less conservative racing (ii) combining racing with sub-sampling
techniques. |
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| MLHII-4 |
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| Title: |
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Personalized News Access |
| Author(s): |
D. G. Kaklamanos,
K. G. Margaritis |
| Abstract: |
PENA (Personalized News Access) is an adaptive
system for the personalized access to news. The aims of the
system are to collect news from predefined news sites, to
select the sections and news in the server that are most relevant
for each user and to present the selected news. In this paper
are described the news collection process, the techniques
adopted for structuring the news archive, the creation, maintenance
and update of the user model and the generation of the personalized
web pages. This work is based on the system that is described
in [1]. |
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| MLHII-5 |
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| Title: |
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A More Accurate Text Classifier
for Positive and Unlabeled Data |
| Author(s): |
Rui-Ming Xin,
Wan-Li Zuo |
| Abstract: |
Purifying unlabeled set and expanding positive
set is central to all LPU (learning from positive and unlabeled
data) approaches. For above two purposes, this paper originally
proposed CoTrain- Active which is a typical two-step approach.
The first step CoTrain, inspired by traditional Co-Training
method, iterates to purify unlabeled set with two individual
SVM classifiers. The second step, active-learning step, further
expands positive set effectively by request the true label
for the “most doubtful positive” instances. Comprehensive
experiment shows that our approach is superior to Biased-SVM
which is said to be previous best. Moreover, CoTrain-Active
is still suitable for those situations when positive instances
are extremely inadequate while unlabeled set contains many
positive instances. |
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