In recent years, machine ... learning, transfer learning and online learning. You need to determine which approach or combination is the most suitable for your algorithm based on the type of ...

We have previously discussed several supervised learning algorithms ... recognition problems in biology and medicine 1. SVM and kNN exemplify several important trade-offs in machine learning ...

Machine learning is enabling investors to tap huge data sets such as social media postings in ways that no mere human could. Yet, despite the enormous potential, its record remains mixed.

One of the main reasons is that many loss functions are too sensitive to sample points far from their classes. In this paper, ...

Machine learning is a branch ... the most common algorithms are Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, and Support Vector Machine (SVM). You can also use ensemble ...

Feature engineering is a hard problem to automate, however, and not all AutoML systems handle it. In summary, machine learning algorithms are just one piece of the machine learning puzzle.

They published their new study in the journal Machine Learning ... to problems beyond the reach of classical computers," adds Le. Looking ahead, Le aims to study how this algorithm can adapt ...

Systems controlled by next-generation computing algorithms could give rise ... physics at The Ohio State University. "The problem with most machine learning-based controllers is that they use ...

Human programmers don't teach machine learning systems how to solve problems, nor do they generally understand the algorithm that a computer devises based on the data. Here is a brief introduction ...