Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experience across learners. Although well studied in human learning, there has been less attention to cooperative transmission of data in machine learning, and we consequently lack strong formal frameworks through which we may reason about the benefits and limitations of cooperative inference. We p...

The use of expert systems is becoming more and more widespread, making the need for appropriate machine learning techniques more acute to help ease the knowledge aquisition bottleneck. Additionally, the increasing number of large databases ooers a vast potential for the automatic generation of new knowledge by machines and its communication to people in a comprehensible form. In response to the...

Many applications of Technology Enhanced Learning are based on strong assumptions: Knowledge needs to be standardized, structured and most of all externalized into learning material that preferably is annotated with meta-data for efficient re-use. A vast body of valuable knowledge does not meet these assumptions, including informal knowledge such as experience and intuition that is key to many ...

PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easyto-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. Implemented algorithms include Long Short-Term Memory (LSTM), policy gradient methods, (multidimensional) recurrent neural networks and deep ...

This article scopes a concept of SVM aggregating intelligence as 3 levels research: aggregating for a better machine learning performance, aggregating for an adaptive/dynamic intelligent system, and aggregating for multitask and life-long continuous machine learning, and reviews existing SVM aggregating methods including SVM ensemble, SVM classification tree, and evolving SVM classification tree.

In this work, several machine learning techniques are presented for nanofiltration modeling. According to the results, specific errors are defined. The rejection due to Nanofiltration increases with pressure but decreases with increasing the concentration of chloride ion. Methods of machine learning represent the rejection of nanofiltration as a function of concentration, pH, pressure and also ...

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received growing attention in the machine learning community over the past decade. The book provides a long-needed, systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted a...

In this paper, we investigate the usage of machine learning in the detection and recognition of double stars. To do this, numerous images including one star and double stars are simulated. Then, 100 terms of Zernike expansion with random coefficients are considered as aberrations to impose on the aforementioned images. Also, a telescope with a specific aperture is simulated. In this work, two k...

Game-playing applications offer various challenges for machine learning, including opening book learning, learning of evaluation functions, player modeling, and others. In this paper, we briefly highlight the most important achievements of this field and summarize some important recent advance.