Machine Learning and Knowledge Extraction, Volume 1, Issue 2

A Support Vector Machine (SVM) for regression is a popular machine learning model that aims to solve nonlinear function approximation problems wherein explicit model equations are difficult to formulate. The performance of an SVM depends largely on t...

The sensitivity of the elbow rule in determining an optimal number of clusters in high-dimensional spaces that are characterized by tightly distributed data points is demonstrated. The high-dimensional data samples are not artificially generated, but...

Graph-embedding algorithms map a graph into a vector space with the aim of preserving its structure and its intrinsic properties. Unfortunately, many of them are not able to encode the neighborhood information of the nodes well, especially from a top...

In many statistical and machine learning applications, without-replacement sampling is considered superior to with-replacement sampling. In some cases, this has been proven, and in others the heuristic is so intuitively attractive that it is taken fo...

A quantifier of similarity is generally a type of score that assigns a numerical value to a pair of sequences based on their proximity. Similarity measures play an important role in prediction problems with many applications, such as statistical lear...

A statistical hypothesis test is one of the most eminent methods in statistics. Its pivotal role comes from the wide range of practical problems it can be applied to and the sparsity of data requirements. Being an unsupervised method makes it very fl...

Natural Language Understanding (NLU) systems are essential components in many industry conversational artificial intelligence applications. There are strong incentives to develop a good NLU capability in such systems, both to improve the user experie...

A Vehicle Make and Model Recognition (VMMR) system can provide great value in terms of vehicle monitoring and identification based on vehicle appearance in addition to the vehicles’ attached license plate typical recognition. A real-time VMMR s...

Sequential Decision Making Problems (SDMPs) that can be modeled as Markov Decision Processes can be solved using methods that combine Dynamic Programming (DP) and Reinforcement Learning (RL). Depending on the problem scenarios and the available Decis...

This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Mu...

Deep neural networks are becoming ubiquitous in text mining and natural language processing, but semantic resources, such as taxonomies and ontologies, are yet to be fully exploited in a deep learning setting. This paper presents an efficient semanti...