Book Chapter10.1007/978-3-642-23672-3_41
Multi-task learning via non-sparse multiple kernel learning
Wojciech Samek,Alexander Binder,Motoaki Kawanabe +2 more
- 29 Aug 2011
- pp 335-342
TL;DR: A novel multi-task learning procedure which extracts useful information from the classifiers for the other categories based on non-sparse multiple kernel learning (MKL) which has been successfully applied to adaptive feature selection for image classification.
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Abstract: In object classification tasks from digital photographs, multiple categories are considered for annotation. Some of these visual concepts may have semantic relations and can appear simultaneously in images. Although taxonomical relations and co-occurrence structures between object categories have been studied, it is not easy to use such information to enhance performance of object classification. In this paper, we propose a novel multi-task learning procedure which extracts useful information fromthe classifiers for the other categories. Our approach is based on non-sparse multiple kernel learning (MKL) which has been successfully applied to adaptive feature selection for image classification. Experimental results on PASCAL VOC 2009 data show the potential of our method.
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Citations
Towards the Internet of Services: The THESEUS Research Program
Wolfgang Wahlster,Hans-Joachim Grallert,Stefan Wess,Hermann Friedrich,Thomas Widenka +4 more
- 02 Sep 2014
TL;DR: This book explains the principal achievements of the Theseus research program, one of the central programs in the German government's Digital 2015 initiative and its High-Tech Strategy 2020, and forms a solid basis for the fourth industrial revolution, the hybrid service economy, and the transformation of big data into useful smart data for the emerging data economy.
60
Multi-task and Lifelong Learning of Kernels
Anastasia Pentina,Shai Ben-David +1 more
- 04 Oct 2015
TL;DR: In this paper, the authors consider the problem of learning kernels for use in SVM classification in the multi-task and lifelong scenarios and provide generalization bounds on the error of a large margin classifier.
56
The joint submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task
Alexander Binder,Wojciech Samek,Marius Kloft,Motoaki Kawanabe +3 more
- 01 Jan 2011
TL;DR: The joint submission of TU Berlin and Fraunhofer FIRST to the ImageCLEF 2011 Photo Annotation Task sought to experiment with extensions of Bag-of-Words (BoW) models at several levels and to apply several kernel-based learning methods recently developed in the group.
Pareto-Path Multitask Multiple Kernel Learning
TL;DR: This work proposes a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives and demonstrates that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.
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References
The Pascal Visual Object Classes (VOC) Challenge
TL;DR: The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Svetlana Lazebnik,Cordelia Schmid,Jean Ponce +2 more
- 17 Jun 2006
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Learning the Kernel Matrix with Semidefinite Programming
Gert R. G. Lanckriet,Nello Cristianini,Peter L. Bartlett,Laurent El Ghaoui,Michael I. Jordan +4 more
TL;DR: This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.
•Proceedings Article
Multi-Task Feature Learning
Andreas Argyriou,Theodoros Evgeniou,Massimiliano Pontil +2 more
- 04 Dec 2006
TL;DR: The method builds upon the well-known 1-norm regularization problem using a new regularizer which controls the number of learned features common for all the tasks, and develops an iterative algorithm for solving it.