Andreas Rauber focuses on Self-organizing map, Artificial intelligence, Multimedia, Structure and Artificial neural network. The concepts of his Self-organizing map study are interwoven with issues in Visualization, Data mining and Human–computer interaction. The Data mining study combines topics in areas such as Representation, Set and Data set.
His Artificial intelligence research includes themes of Machine learning, Task, Pattern recognition and Natural language processing. His research investigates the connection between Multimedia and topics such as Convolutional neural network that intersect with problems in Deep learning. His Information retrieval research focuses on Music information retrieval and how it relates to Musical composition and Popular music.
Andreas Rauber mainly focuses on Information retrieval, World Wide Web, Artificial intelligence, Digital preservation and Data mining. His Information retrieval research incorporates themes from Music information retrieval and Cluster analysis. His World Wide Web study incorporates themes from Multimedia and Interface.
Andreas Rauber has included themes like Machine learning, Pattern recognition and Natural language processing in his Artificial intelligence study. The study incorporates disciplines such as Set and Data set in addition to Data mining. His work on Self-organizing map as part of general Artificial neural network study is frequently linked to Structure, bridging the gap between disciplines.
His primary areas of investigation include Artificial intelligence, Information retrieval, Data science, World Wide Web and Data mining. His study in Artificial intelligence focuses on Artificial neural network in particular. Andreas Rauber interconnects Recall, Graph and Information system in the investigation of issues within Information retrieval.
Andreas Rauber focuses mostly in the field of Data science, narrowing it down to matters related to Context and, in some cases, Workflow. Andreas Rauber does research in World Wide Web, focusing on Digital preservation specifically. His Data mining research integrates issues from Data citation and Data set.
His primary areas of study are Information retrieval, Artificial intelligence, Convolutional neural network, Reproducibility and Identification. His Information retrieval study which covers Domain that intersects with Speech recognition and Support vector machine. His study connects Natural language processing and Artificial intelligence.
His research in Convolutional neural network intersects with topics in Multimedia and Task. His Multimedia research is multidisciplinary, incorporating perspectives in Visual music, Pop music automation and Music information retrieval. Andreas Rauber usually deals with Artificial neural network and limits it to topics linked to Layer and Metadata.
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The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
A. Rauber;D. Merkl;M. Dittenbach.
IEEE Transactions on Neural Networks (2002)
Content-based organization and visualization of music archives
Elias Pampalk;Andreas Rauber;Dieter Merkl.
acm multimedia (2002)
The growing hierarchical self-organizing map
M. Dittenbach;D. Merkl;A. Rauber.
international joint conference on neural network (2000)
Using smoothed data histograms for cluster visualization in Self-Organizing Maps
Elias Pampalk;Andreas Rauber;Dieter Merkl.
Lecture Notes in Computer Science (2002)
EVALUATION OF FEATURE EXTRACTORS AND PSYCHO-ACOUSTIC TRANSFORMATIONS FOR MUSIC GENRE CLASSIFICATION
Thomas Lidy;Andreas Rauber.
international symposium/conference on music information retrieval (2005)
Uncovering hierarchical structure in data using the growing hierarchical self-organizing map
Michael Dittenbach;Andreas Rauber;Dieter Merkl.
Systematic planning for digital preservation: evaluating potential strategies and building preservation plans
Christoph Becker;Hannes Kulovits;Mark Guttenbrunner;Stephan Strodl.
International Journal on Digital Libraries (2009)
Using Psycho-Acoustic Models and Self-Organizing Maps to Create a Hierarchical Structuring of Music by Musical Styles.
Andreas Rauber;Elias Pampalk;Dieter Merkl.
international symposium/conference on music information retrieval (2002)
How to choose a digital preservation strategy: evaluating a preservation planning procedure
Stephan Strodl;Christoph Becker;Robert Neumayer;Andreas Rauber.
acm/ieee joint conference on digital libraries (2007)
RHYME AND STYLE FEATURES FOR MUSICAL GENRE CLASSIFICATION BY SONG LYRICS
Rudolf Mayer;Robert Neumayer;Andreas Rauber.
international symposium/conference on music information retrieval (2008)
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