We consider the problem of multiclass classification. Our main thesis is that a simple" one-vs-
all" scheme is as accurate as any other approach, assuming that the underlying binary …

This paper reviews and extends the field of similarity-based classification, presenting new
analyses, algorithms, data sets, and a comprehensive set of experimental results for a rich …

In the past decade the mathematical theory of machine learning has lagged far behind the
triumphs of deep neural networks on practical challenges. However, the gap between theory …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Modern neural architectures for classification tasks are trained using the cross-entropy loss,
which is widely believed to be empirically superior to the square loss. In this work we …

We compare classification and regression tasks in an overparameterized linear model with
Gaussian features. On the one hand, we show that with sufficient overparameterization all …

This chapter focuses on providing a complete grasp of the foundations of machine learning
(ML). Machine learning is a rapidly evolving domain with wide-ranging applications, from …

In this paper, we propose an efficient algorithm, called vector field consensus, for
establishing robust point correspondences between two sets of points. Our algorithm starts …

Recent advances in mobile health have produced several new models for inferring stress
from wearable sensors. But, the lack of a gold standard is a major hurdle in making clinical …