نتایج جستجو برای: including machine learning

تعداد نتایج: 1651998  

2018
Miroslava Cuperlovic-Culf

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of mac...

Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...

Due to the growth of the aging phenomenon, the use of intelligent systems technology to monitor daily activities, which leads to a reduction in the costs for health care of the elderly, has received much attention. Considering that each person's daily activities are related to his/her moods, thus, the relationship can be modeled using intelligent decision-making algorithms such as machine learn...

2017
Tie-Yan Liu Wei Chen Taifeng Wang

In recent years, artificial intelligence has achieved great success in many important applications. Both novel machine learning algorithms (e.g., deep neural networks), and their distributed implementations play very critical roles in the success. In this tutorial, we will first review popular machine learning algorithms and the optimization techniques they use. Second, we will introduce widely...

2017
Yibo Lin Xiaoqing Xu Jiaojiao Ou David Z. Pan

Machine learning is a powerful computer science technique that can derive knowledge from big data and make predictions/decisions. Since nanometer integrated circuits (IC) and manufacturing have extremely high complexity and gigantic data, there is great opportunity to apply and adapt various machine learning techniques in IC physical design and verification. This paper will first give an introd...

2001
Mario Marchand John Shawe-Taylor

We generalize the classical algorithms of Valiant and Haussler for learning conjunctions and disjunctions of Boolean attributes to the problem of learning these functions over arbitrary sets of features; including features that are constructed from the data. The result is a general-purpose learning machine, suitable for practical learning tasks, that we call the Set Covering Machine. We present...

Journal: :CoRR 2018
Liang Xiao Xiaoyue Wan Xiaozhen Lu Yanyong Zhang Di Wu

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques includi...

2007
G. Charpiat M. Hofmann B. Schölkopf

We introduce machine learning techniques, more specifically kernel methods, and show how they can be used for medical imaging. After a tutorial presentation of machine learning concepts and tools, including Support Vector Machine (SVM), kernel ridge regression and kernel PCA, we present an application of these tools to the prediction of Computed Tomography (CT) images based on Magnetic Resonanc...

A. Barzigar, D. Rezazadeh, M. Shahrouzi,

Opposition-based learning was first introduced as a solution for machine learning; however, it is being extended to other artificial intelligence and soft computing fields including meta-heuristic optimization. It not only utilizes an estimate of a solution but also enters its counter-part information into the search process. The present work applies such an approach to Colliding Bodies Optimiz...

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