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 کاملProbabilistic Active Learning with Structure-Sensitive Kernels
This work proposes two approaches to improve the poolbased active learning strategy ’Multi-Class Probabilistic Active Learning ’ (McPAL) by using two kernel functions based on Gaussian mixture models (GMMs). One uses the kernels for the instance selection of the McPAL strategy, the second employs them in the classification step. The results of the evaluation show that using a different classifi...
متن کاملActive constrained fuzzy clustering: A multiple kernels learning approach
In this paper, we address the problem of constrained clustering along with active selection of clustering constraints in a unified framework. To this aim, we extend the improved possibilistic c-Means algorithm (IPCM) with a multiple kernels learning setting under supervision of side information. By incorporating multiple kernels, the limitation of improved possibilistic c-means to spherical clu...
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 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: IEEE Transactions on Neural Networks and Learning Systems
سال: 2021
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2020.3047953