S
Sandor Szedmak
Researcher at Helsinki Institute for Information Technology
Publications - 86
Citations - 5745
Sandor Szedmak is an academic researcher from Helsinki Institute for Information Technology. The author has contributed to research in topics: Support vector machine & Margin (machine learning). The author has an hindex of 22, co-authored 83 publications receiving 5157 citations. Previous affiliations of Sandor Szedmak include University of Innsbruck & University of Helsinki.
Papers
More filters
Journal ArticleDOI
Canonical Correlation Analysis: An Overview with Application to Learning Methods
TL;DR: A general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text and compares orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model is presented.
Book ChapterDOI
The 2005 PASCAL visual object classes challenge
Mark Everingham,Andrew Zisserman,Christopher Williams,Luc Van Gool,Moray Allan,Christopher M. Bishop,Olivier Chapelle,Navneet Dalal,Thomas Deselaers,Gyuri Dorkó,Stefan Duffner,J Eichhorn,Jason Farquhar,Mario Fritz,Christophe Garcia,Tom Griffiths,Frédéric Jurie,Daniel Keysers,Markus Koskela,Jorma Laaksonen,Diane Larlus,Bastian Leibe,Hongying Meng,Hermann Ney,Bernt Schiele,Cordelia Schmid,Edgar Seemann,John Shawe-Taylor,Amos Storkey,Sandor Szedmak,Bill Triggs,Ilkay Ulusoy,Ville Viitaniemi,Jianguo Zhang +33 more
TL;DR: The PASCAL Visual Object Classes Challenge (PASCALVOC) as mentioned in this paper was held from February to March 2005 to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects).
Proceedings Article
Two view learning: SVM-2K, Theory and Practice
TL;DR: This paper proposes a method that combines this two stage learning (KCCA followed by SVM) into a single optimisation termed SVM-2K and presents both experimental and theoretical analysis of the approach showing encouraging results and insights.
Journal Article
Kernel-Based Learning of Hierarchical Multilabel Classification Models
TL;DR: A kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time and its predictive accuracy was found to be competitive with other recently introduced hierarchical multi-category or multilabel classification learning algorithms.
Journal ArticleDOI
Depressive symptomatology and vital exhaustion are differentially related to behavioral risk factors for coronary artery disease.
TL;DR: Vital exhaustion is associated with perceived cardiovascular complaints and history of cardiovascular treatment, whereas depressive symptomatology seems to be more closely connected to disabilities and complaints related to alcohol, drug, and congenital-disorder, and to dysfunctional cognitions and hostility.