IEEE

A Semiproximal Support Vector Machine Approach for Binary Multiple Instance Learning

Abstract: We face a binary multiple instance learning (MIL) problem, whose objective is to discriminate between two kinds of point sets: positive and negative. In the MIL terminology, such sets are ...
IEEE

Learning the Optimal Discriminant SVM With Feature Extraction

Abstract: Subspace learning and Support Vector Machine (SVM) are two critical techniques in pattern recognition, playing pivotal roles in feature extraction and classification. However, how to learn ...
Scientific Research Publishing

Predictive Analytics for Project Risk Management Using Machine Learning ()

1 Pharmavite LLC, Los Angeles, CA, USA. 2 Microsoft, Charlotte, NC, USA. 3 AXS Group LLC, Los Angeles, CA, USA. 4 TCS, Indianapolis, IN, USA. Risk management is relevant for every project that which ...
InfoWorld

The engines of AI: Machine learning algorithms explained

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...
InfoWorld

14 popular AI algorithms and their uses

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...
Frontiers

Machine learning approaches to identify Parkinson's disease using voice signal features

Parkinson's Disease (PD) is the second most common age-related neurological disorder that leads to a range of motor and cognitive symptoms. A PD diagnosis is difficult since its symptoms are quite ...
Frontiers

Editorial: Machine learning for biological sequence analysis

Biomacromolecules, primarily proteins, DNA, and RNA, are crucial for vital physiological processes. Biomacromolecules can generally be represented by sequences, comprising series of strings, which are ...