62,099 research outputs found
Feature Relevance Bounds for Linear Classification
Göpfert C, Pfannschmidt L, Hammer B. Feature Relevance Bounds for Linear Classification. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2017: 187--192.Biomedical applications often aim for an identification of relevant features for a given classification task, since these carry the promise of semantic insight into the underlying process.
For correlated input dimensions, feature relevances are not unique, and the identification of meaningful subtle biomarkers remains a challenge.
One approach is to identify intervals for the possible relevance of given features, a problem related to all relevant feature determination.
In this contribution, we address the important case of linear classifiers and we transfer the problem how to infer feature relevance bounds to a convex optimization problem.
We demonstrate the superiority of the resulting technique in comparison to popular feature-relevance determination methods in several benchmarks
The Benefit of Multitask Representation Learning
We discuss a general method to learn data representations from multiple
tasks. We provide a justification for this method in both settings of multitask
learning and learning-to-learn. The method is illustrated in detail in the
special case of linear feature learning. Conditions on the theoretical
advantage offered by multitask representation learning over independent task
learning are established. In particular, focusing on the important example of
half-space learning, we derive the regime in which multitask representation
learning is beneficial over independent task learning, as a function of the
sample size, the number of tasks and the intrinsic data dimensionality. Other
potential applications of our results include multitask feature learning in
reproducing kernel Hilbert spaces and multilayer, deep networks.Comment: To appear in Journal of Machine Learning Research (JMLR). 31 page
A PAC-Bayesian bound for Lifelong Learning
Transfer learning has received a lot of attention in the machine learning
community over the last years, and several effective algorithms have been
developed. However, relatively little is known about their theoretical
properties, especially in the setting of lifelong learning, where the goal is
to transfer information to tasks for which no data have been observed so far.
In this work we study lifelong learning from a theoretical perspective. Our
main result is a PAC-Bayesian generalization bound that offers a unified view
on existing paradigms for transfer learning, such as the transfer of parameters
or the transfer of low-dimensional representations. We also use the bound to
derive two principled lifelong learning algorithms, and we show that these
yield results comparable with existing methods.Comment: to appear at ICML 201
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