Tech Xplore

Consistency, not complexity, is the key to teaching robots dexterity, new research suggests

Teaching robots to manipulate objects with humanlike dexterity has long been one of robotics' toughest challenges. Tasks such ...
IEEE

DHS-AE: A Distributed Support Vector Machine With Adaptive Regularization Parameters for Different Data Distributions

Abstract: In distributed machine learning scenarios, the difference in data distribution among different nodes is a key issue that cannot be ignored. However, existing methods make it difficult to ...
Princeton University

Selected Lectures on Statistical Machine Learning

Abstract: The effect of relative entropy asymmetry is analyzed in the context of empirical risk minimization (ERM) with relative entropy regularization (ERM-RER). We show that regularization by ...
Scientific Research Publishing

Belkin, M., Niyogi, P. and Sindhwani, V. (2006) Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Journal of Machine Learning ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...
Quanta Magazine

What Is Machine Learning?

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...
Forbes

The Vital Difference Between Machine Learning And Generative AI

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...