Operator-valued kernels for learning from functional response data
In this paper we consider the problems of supervised classification and regression in the
case where attributes and labels are functions: a data is represented by a set of functions …
case where attributes and labels are functions: a data is represented by a set of functions …
Use of kernel deep convex networks and end-to-end learning for spoken language understanding
We present our recent and ongoing work on applying deep learning techniques to spoken
language understanding (SLU) problems. The previously developed deep convex network …
language understanding (SLU) problems. The previously developed deep convex network …
A unifying framework in vector-valued reproducing kernel hilbert spaces for manifold regularization and co-regularized multi-view learning
This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS)
framework for the problem of learning an unknown functional dependency between a …
framework for the problem of learning an unknown functional dependency between a …
A unifying framework for vector-valued manifold regularization and multi-view learning
This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS)
formulation for the problem of learning an unknown functional dependency between a …
formulation for the problem of learning an unknown functional dependency between a …
Operator-valued kernel-based vector autoregressive models for network inference
Reverse-engineering of high-dimensional dynamical systems from time-course data still
remains a challenging and important problem in knowledge discovery. For this learning task …
remains a challenging and important problem in knowledge discovery. For this learning task …
Two-sample test with kernel projected wasserstein distance
We develop a kernel projected Wasserstein distance for the two-sample test, an essential
building block in statistics and machine learning: given two sets of samples, to determine …
building block in statistics and machine learning: given two sets of samples, to determine …
A generalized kernel approach to structured output learning
We study the problem of structured output learning from a regression perspective. We first
provide a general formulation of the kernel dependency estimation (KDE) approach to this …
provide a general formulation of the kernel dependency estimation (KDE) approach to this …
Operator-valued Bochner theorem, Fourier feature maps for operator-valued kernels, and vector-valued learning
HQ Minh - arxiv preprint arxiv:1608.05639, 2016 - arxiv.org
This paper presents a framework for computing random operator-valued feature maps for
operator-valued positive definite kernels. This is a generalization of the random Fourier …
operator-valued positive definite kernels. This is a generalization of the random Fourier …
Multiple operator-valued kernel learning
Positive definite operator-valued kernels generalize the well-known notion of reproducing
kernels, and are naturally adapted to multi-output learning situations. This paper addresses …
kernels, and are naturally adapted to multi-output learning situations. This paper addresses …
[PDF][PDF] Support distribution machines
Most machine learning algorithms, such as classification or regression, treat the individual
data point as the object of interest. Here we consider extending machine learning algorithms …
data point as the object of interest. Here we consider extending machine learning algorithms …