Over the past few decades, Machine Learning (ML) has evolved from the endeavour of few computer enthusiasts exploiting the possibility of computers learning to play games, and a part of Mathematics (Statistics) that seldom considered computational approaches, to an independent research discipline that has not only provided the necessary base for statistical-computational principles of learning ...

In automated multi-label text categorization, an automatic categorization system should output a label set, whose size is unknown a priori, for each document under analysis. Many machine learning techniques have been used for building such automatic text categorization systems. In this paper, we examine virtual generalizing random access memory weightless neural networks (VG-RAM WNN), an effect...

We show that by using machine learning techniques (genetic programming, in particular), Euler’s famous identity (V −E+F = 2) can be automatically discovered from a limited amount of data indicating the values of V , E, and F for a small number of polyhedra—the five platonic solids. This result suggests that mechanized inductive techniques have an important role to play in the process of doing c...

The "Brainstorming" approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedu...

Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range shift in organism, range of invasive species influenced by climate change are important parameters in understanding the impact of climate change. However, success of machine learning-based approache...

This editorial overviews the contents of Special Issue “Machine Learning for Energy Systems 2021” and review trends in machine learning (ML) techniques energy system (ES) optimization [...]

Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant materi...

We compare two methods, Anchored Learning, and a simple new method (Hyperplane Distance), for finding Hard To Learn examples in Machine Learning tasks that use SVMs. These Hard To Learn examples can be corpus errors, or examples which are difficult to predict with the given set of features. The Anchored Learning method is extended to deal with various kernels and SV regression. A number of expe...

Applications of Machine Learning (ML) to stock market analysis include Portfolio Optimization, Investment Strategy Determination, and Market Risk Analysis. This paper focuses on the problem of Investment Strategy Determination through the use of reinforcement learning techniques. Four techniques, two based on Recurrent Reinforcement Learning (RLL) and two based on Q-learning, were utilized. Q-l...

There is a lot of attention paid to parameter setting in supervised machine learning elds. Despite ML unsupervised systems also having many parameters to set, there has not as yet been much attention focused on the subject. In this report we summarise the major types of unsupervised systems and discuss the diierent concept quality measures used in unsupervised systems. An automatic parameter se...