5 papers · 1 filter
A signal separation view of classification
H. N. Mhaskar, Ryan O'Dowd
The problem of classification in machine learning has often been approached in terms of function approximation. In this paper, we propose an alternative approach for classification…
An Approximation Theory Perspective on Machine Learning
Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
A central problem in machine learning is often formulated as follows: Given a dataset , which is a sample drawn from an unknown probability distribution, th…
Active Learning Classification from a Signal Separation Perspective
Hrushikesh Mhaskar, Ryan O'Dowd, Efstratios Tsoukanis
In machine learning, classification is usually seen as a function approximation problem, where the goal is to learn a function that maps input features to class labels. In this pap…
Approximation by non-symmetric networks for cross-domain learning
Hrushikesh Mhaskar
For the past 30 years or so, machine learning has stimulated a great deal of research in the study of approximation capabilities (expressive power) of a multitude of processes, suc…
Data Complexity Estimates for Operator Learning
Nikola B. Kovachki, Samuel Lanthaler, Hrushikesh Mhaskar
Operator learning has emerged as a new paradigm for the data-driven approximation of nonlinear operators. Despite its empirical success, the theoretical underpinnings governing the…