Early detection is critical to improving outcomes across many diseases, yet cliniciansmust rapidly interpret heterogeneous signals, reports, and images. Automated analysis helps uncover subtle ...

Abstract: Support vector machines are a family of data analysis algorithms based on convex quadratic programming. We focus on their use for classification: in that case, the SVM algorithms work by ...

Abstract: In order to accurately and effectively diagnose the transmission line faults of the power system, a genetic-algorithm support vector machine and the D-S evidence theory based fault ...

In yesterday's article, we defined the primal optimization goal that SVM aims to solve. If you haven't read it yet, I highly recommend you do so first to build the foundation for today's discussion.

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

In the rapidly evolving landscape of business analytics, machine learning algorithms have become indispensable tools for extracting insights, making predictions, and automating decision-making ...

Space complexity of machine learning algorithms is the amount of memory or storage an algorithm requires for its successful execution. This becomes one of the important metrics of concern since it ...

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, ...

The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 ...

We trained models using logistic regression (LR) and four commonly used ML algorithms to predict NCGC from age-/sex-matched controls in two EHR systems: Stanford University and the University of ...

Financial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but identification of need is difficult. We ...