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Anwendungen Kognitiver Algorithmen

Blockseminar

Termine und Dozenten

Termin:	Präsentationen 04.07.2012, 10:00 - 13:00
Termin:	Raum: FR 6046
Verantwortlicher:	Prof. Dr. Klaus-Robert Müller
Betreuer:	Dr. Felix Bießmann, Irene Winkler

Themen

Computerprogramme können nützliche kognitive Fähigkeiten lernen. Dieses Seminar erlaubt eine vertiefende Einarbeitung in spezielle Anwendungsgebiete von Algorithmen aus dem Bereich des Maschinellen Lernens wie etwa automatisierte Informationsextraktion aus Texten, Mustererkennung in hochdimensionalen Daten, explorative Datenanalyse.

Unter Anleitung wird englischsprachige Fachliteratur über ausgewählte Anwendungsbeispiele zu analysieren, kritisch zu evaluieren und verständlich zu präsentieren sein.

Voraussetzungen

Grundlegende Konzepte der Wahrscheinlichkeitsrechnung, Statistik und Linearer Algebra.

Slides der Vorbesprechung

Enthält alle Abstracts der zur Auswahl stehenden Papers. Slides

Papers

Thema

Student

Betreuer

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A simplified neuron model as a principal component analyzer	Tongxin Son
Spatiotemporal energy models for the perception of motion
Eigenfaces for Recognition	Min Zheng	Wojciech Samek
Emergence of simple-cell receptive field properties by learning a sparse code for natural images	Gregor Hendel

$/!\$ Edit conflict - your version:

A simplified neuron model as a principal component analyzer	Tongxin Son	Duncan Blythe
Spatiotemporal energy models for the perception of motion
Eigenfaces for Recognition	Min Zheng	Wojciech Samek
Emergence of simple-cell receptive field properties by learning a sparse code for natural images	Gregor Hendel	Duncan Blythe

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Gradient-Based Learning Applied to Document Recognition
Matrices, Vector Spaces, and Information Retrieval	David Lassner
Support vector machines for spam categorization	Chengbing Liu

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Kernel PCA and De--Noising in Feature Spaces	Sha Huang
Unsupervised Learning by Probabilistic Latent Semantic Analysis
Rapid Object Detection using a Boosted Cascade of Simple Features	Siyun Li	Wojciech Samek
Kernel methods for exploratory pattern analysis: a demonstration on text data	Yi Ding
The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects
Decoding the visual and subjective contents of the human brain

$/!\$ Edit conflict - your version:

Kernel PCA and De--Noising in Feature Spaces	Sha Huang	Bettina Mieth
Unsupervised Learning by Probabilistic Latent Semantic Analysis
Rapid Object Detection using a Boosted Cascade of Simple Features	Siyun Li	Wojciech Samek
Kernel methods for exploratory pattern analysis: a demonstration on text data	Yi Ding	Felix Bießmann
The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects
Decoding the visual and subjective contents of the human brain

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|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Oja1982.pdf|A simplified neuron model as a principal component analyzer]] || Tongxin Son || [[mailto:Duncan Blythe duncan.blythe@bccn-berlin.de|Duncan Blythe]] ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Adelson1985.pdf|Spatiotemporal energy models for the perception of motion]] ||  ||  ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Turk1991.pdf|Eigenfaces for Recognition]]|| Min Zheng||  [[wojciech.samek@campus.tu-berlin.de|Wojciech Samek]] ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Olshausen1996.pdf|Emergence of simple-cell receptive field properties by learning a sparse code for natural images]]|| Gregor Hendel || [[mailto:Duncan Blythe duncan.blythe@bccn-berlin.de|Duncan Blythe]] ||

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|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Mika1998.pdf|Kernel PCA and De--Noising in Feature Spaces]]|| Sha Huang|| [[bettina.mieth@tu-berlin.de|Bettina Mieth]]||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Hofmann2001.pdf|Unsupervised Learning by Probabilistic Latent Semantic Analysis]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Viola2001.pdf|Rapid Object Detection using a Boosted Cascade of Simple Features]]||Siyun Li || [[wojciech.samek@campus.tu-berlin.de|Wojciech Samek]]||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/DeBie2004.pdf|Kernel methods for exploratory pattern analysis: a demonstration on text data]]|| Yi Ding || [[felix.biessmann@tu-berlin.de|Felix Bießmann]] ||
|| [[ http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Blankertz_2007_Neuroimage.pdf| The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects]]|| || ||
|| [[http://www.user.tu-berlin.de/felix.biessmann/downloads/AKA/Kamitani_2005.pdf|Decoding the visual and subjective contents of the human brain]]|| || ||

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