Recently, there is a new trend which is applying the machine learning theory to communications [26].
Machine learning theory states that with unlimited data, we could expect all algorithms to produce similar-quality results.
The advantages of machine learning theory and how it can be used to assess the quality of a given model (or learning machine) are discussed.
SVMs are a set of related supervised learning methods used for classification and regression (Gunn1998) which use machine learning theory to maximize predictive accuracy while automatically avoiding over-fit to the data.
There are various image classification techniques, serving different features for different purposes, and the Support Vector Machine (SVM) classification method based on statistical machine learning theory stands out to be an effective and accurate image classification technique among them.
In this paper, we present a new intelligent forensics approach that incorporates the advantages of artificial intelligence and machine learning theory to automatically flag new/previously unseen CSA media to investigators.
Other more specialized classes are sometimes offered including machine learning itself, learning theory, computer vision, robotics, bioinformatics, game theory, neurobiology, artificial intelligence, deep learning, Bayesian networks, causality, etc. Q: Whose or what lab's research in machine learning do you find the most interesting?
This article presents an overview of the SmartSite research project that adopts machine learning, decision theory and distributed artificial intelligence to design and test a multi-agent system (MAS) for asphalt road construction.
Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors.
Many of the methods of analyzing time-series data originated from various disciplines, such as signal processing, dynamic system theory, machine learning and information theory, and have been applied to detect differentially expressed genes, identify expression patterns, and construct gene networks [ 15, 21- 23], nevertheless challenges remain.
His research interests include social computing, machine learning, and Graph Theory.
