We consider regularized least-squares (RLS) with a Gaussian kernel. Weprove that if we let the Gaussian bandwidth $\sigma \rightarrow\infty$ while letting the regularization parameter $\lambda\rightarrow 0$, the RLS solution ...

We present AvatarSAT, a SAT solver that uses machine-learning classifiers to automatically tune the heuristics of an off-the-shelf SAT solver on a per-instance basis. The classifiers use features of both the input and ...

In cloud-computing systems, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. Most previous work on cloud-bandwidth guarantees has assumed that cloud tenants know ...

Breast cancer is a prevalent disease that affects mostly women, an early diagnosis will expedite the treatment of this ailment. In recent times, Machine Learning (ML) techniques have been employed in biomedical and informatics ...

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special ...

Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie ["Regularization and variable selection via the elastic net" J. R. Stat. ...

Phishing is a social engineering attack that has been perpetuated for long and is still a prominent attack with an attending high number of victims. The adverse effect of this allows phishers easy access to sensitive ...

Phishing is a social engineering attack that has been perpetuated for long and is still a prominent attack with an attending high number of victims. The adverse effect of this allows phishers easy access to sensitive ...

Clustering methods optimize the partitioning of data points with respect to an internal metric, such as likelihood, in order to approximate the goodness of clustering. However, this internal metric does not necessarily ...

This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" ...

We study multi-category classification in the framework of computational learning theory. We show how a relaxation approach, which is commonly used in binary classification, can be generalized to the multi-class setting. ...

Large data sets are often modeled as being noisy samples from probability distributions in R^D, with D large. It has been noticed that oftentimes the support M of these probability distributions seems to be well-approximated ...

We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view ...

In this work we are interested in the problems of supervised learning and variable selection when the input-output dependence is described by a nonlinear function depending on a few variables. Our goal is to consider a ...

A crucial challenge in robotics is achieving reliable results in spite of sensing and control uncertainty. A prominent strategy for dealing with uncertainty is to construct a feedback policy, where actions are chosen as a ...

The Heart Disease according to the survey is the leading cause of death all over the world. The health sector has a lot of data, but unfortunately, these data are not well utilized. This is as a result of lack of effective ...