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A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this...

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad...

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors ...

My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiari...

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be...

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are t...

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-...

Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-learning techniques with likelihood-based inference tests, allows estimating the experimental sensitivity of high-dimensional data sets. Here...

With the constant advancement of the smart grid, zero-day attack processes in Advanced Metering Infrastructure (AMI) and Supervisory Control and Data Acquisition (SCADA) networks are also continually evolving. This study...

Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new ...

Laboratory practice is a fundamental aspect of computer network learning. Experiments tend to be very specific, frequently demanding changes in the local network topology and privileged access to the operating system con...

Subjects / keywords: Machine learning; Artificial intelligence; Electronic books

Subjects / keywords: Neural networks (Computer science); Machine learning; Electronic books

Subjects / keywords: Data mining; Machine learning; Python (Computer program language)

Subjects / keywords: R (Computer program language); Statistics; Data processing; Data science; Machine learning; Information visualization; Databases; Management

Subjects / keywords: Neural networks (Computer science); Artificial intelligence; Information networks; Machine learning

Subjects / keywords: Artificial intelligence; Machine learning; Bioinformatics; Biotechnology; Pharmacology

Subjects / keywords: Computer science; Technological innovations; Machine learning; Artificial intelligence

Subjects / keywords: TensorFlow; Machine learning; Artificial intelligence

With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding ...

Subjects / keywords: Multi-task learning; Computer science; Artificial intelligence; Machine learning; Inductive transfer; Instance-based learning; Generalization; Transfer of learning; Task (project management); Inductive bias; Feature lear...

To test the hypothesis that lecturing maximizes learning and course performance, we metaanalyzed 225 studies that reported data on examination scores or failure rates when comparing student performance in undergraduate s...

We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged enco...

Subjects / keywords: AHMAD, IMRAN; MARCOMBO; 005.1 A286a2