6 papers
Learning Without Training
Ryan O'Dowd
Machine learning is at the heart of managing the real-world problems associated with massive data. With the success of neural networks on such large-scale problems, more research i…
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…
Aspects of a Generalized Theory of Sparsity based Inference in Linear Inverse Problems
Raghu G. Raj, Ryan O'Dowd, Owen T. Huber +1
Linear inverse problems are ubiquitous in various science and engineering disciplines. Of particular importance in the past few decades, is the incorporation of sparsity based prio…
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…
Learning on manifolds without manifold learning
H. N. Mhaskar, Ryan O'Dowd
Function approximation based on data drawn randomly from an unknown distribution is an important problem in machine learning. The manifold hypothesis assumes that the data is sampl…
Local transfer learning from one data space to another
H. N. Mhaskar, Ryan O'Dowd
A fundamental problem in manifold learning is to approximate a functional relationship in a data chosen randomly from a probability distribution supported on a low dimensional sub-…