Submissions Due:  April  4,  2010=

=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

1 - 4 June, 2010
=0ABrest State Technical Univer= sity
=0ABelarus

http://icnnai.bstu.by/icnnai= -2010.html

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= SCOPE
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Incremental Learning is a subfield of the Artificial=0AInte= lligence that deals with data flow. The key hypothesis is that the=0Aalgori= thms are able to learn data from a data subset and then to re-learn with=0A= new unlabeled data. At the end of the learning, one of the problems is the= =0Aclustering analysis and visualization of the results. The topological le= arning=0Ais one of the most known technique that allows clustering and visu= alization=0Asimultaneously. At the end of the topographic learning, "simila= r'' data=0Awill be collect in clusters, which correspond to the sets of sim= ilar=0Aobservations. These clusters can be represented by more concise info= rmation=0Athan the brutal listing of their patterns, such as their gravity = center or=0Adifferent statistical moments. As expected, this information is= easier to=0Amanipulate than the original data points.

=0ADimensionality reduction is another major challeng= e=0Ain the domain of unsupervised learning which deals with the transformat= ion of a=0Ahigh dimensional dataset into a low dimensional space, while ret= aining most of=0Athe useful structure in the original data, retaining only = relevant features and=0Aobservations. Dimensionality reduction can be achie= ved by using a clustering=0Atechnique to reduce the number of observations = or a features selection approach=0Ato reduce the features space.

This session would solicit theoretical an= d applicative=0Aresearch papers including but not limited to the following = topics :

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=0A        =           =0A    &nb= sp;=B7 Supervised/Unsupervised Topological Learning;
=0A   &n= bsp;              = =0A     =B7 Self-Organization (based on artificial neur= al=0Anetworks, but not limited to);
=0A       =            =0A  &nbs= p;  =B7 Clustering Visualization and Analysis;
=0A   &nb= sp;              =0A=      =B7 Time during the learning process;
=0A = ;               &nbs= p; =0A     =B7 Memory based systems;
=0A =                 = ; =0A     =B7 User interaction models;
=0A&nbs= p;               &nb= sp; =0A     =B7 Fusion (Consensus) based models; <= br>=0A              =    =0A     =B7 Clustering;
=0A = ;               &nbs= p; =0A     =B7 Feature selection;