منابع مشابه
Metric Learning with Multiple Kernels
Metric learning has become a very active research field. The most popular representative–Mahalanobis metric learning–can be seen as learning a linear transformation and then computing the Euclidean metric in the transformed space. Since a linear transformation might not always be appropriate for a given learning problem, kernelized versions of various metric learning algorithms exist. However, ...
متن کاملLearning vector fields by kernels
This paper proposes a novel technique for reconstructing a vector field from unstructured samples. Contrarily to surface reconstruction, which search for local and global coherence, vector field reconstruction must determine a locally differentiable vector field from a very small number of samples. In this work, this problem is formulated as a machine-learning problem, training the machine on t...
متن کاملOnline Learning with (Multiple) Kernels: A Review
This review examines kernel methods for online learning, in particular, multiclass classification. We examine margin-based approaches, stemming from Rosenblatt's original perceptron algorithm, as well as nonparametric probabilistic approaches that are based on the popular gaussian process framework. We also examine approaches to online learning that use combinations of kernels--online multiple ...
متن کاملOnline Learning with Multiple Operator-valued Kernels
We consider the problem of learning a vector-valued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of operator-valued kernels. We describe two online algorithms for learning f while taking into account the output structure. A first contribution is an algorithm, ONORMA, that extends the standard kernel-based online learning algorithm NOR...
متن کاملAccurate Interatomic Force Fields via Machine Learning with Covariant Kernels
Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Stochastic Environmental Research and Risk Assessment
سال: 2010
ISSN: 1436-3240,1436-3259
DOI: 10.1007/s00477-010-0405-0