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Artificial intelligence is getting a foothold in medicine for disease screening and diagnosis. While typical machine learning methods require large labeled datasets for training and validation, their application is limited in clinical ...

• Application of unsupervised learning for automated clustering of spectra profiles.

Multi-view data has become fairly important since large amounts of information are constantly generated from different sources. So far, most multi-view research on unsupervised learning has focused on clustering algorithms suitable for ...

• The paper provides two families of multi-view batch SOM algorithms.
• The ...

In a supervised setting, the global classification paradigm leverages the whole training data to produce a single class discriminative model. Alternatively, the local classification approach builds multiple base classifiers, each of ...

• Regional classifiers reduces a global problem into multiple simpler sub-problems.

With the rapidly increasing size of digitized and born-digital multimedia collections in archives, museums and private collections, manually curating collections becomes a nearly impossible task without disregarding large parts of the collection. ...

Learners’ satisfaction with Massive Open Online Courses (MOOCs) has been evaluated through quantitative approaches focusing on survey-based methods in several studies. User-Generated Content (UGC) has been an effective approach to assess users’ ...

This paper explores the implementation of a distributed model of the Self-Organizing Map (SOM) and its subsequent validation through the implementation of a proof-of-concept prototype using the Typed-Akka tool kit. This work is a part of an on-going ...

Due to the high accuracy of the K nearest neighbor algorithm in different problems, KNN is one of the most important classifiers used in data mining applications and is recognized in the literature as a benchmark algorithm. Despite its high ...

This work presents a mathematical treatment of the relation between Self-Organizing Maps (SOMs) and Gaussian Mixture Models (GMMs). We show that energy-based SOM models can be interpreted as performing gradient descent, minimizing an approximation ...

Self-Organizing Maps (SOM) are well-known unsupervised neural networks able to perform vector quantization while mapping an underlying regular neighbourhood structure onto the codebook. They are used in a wide range of applications. As with most ...

• Introducing a hybrid dimensionality reduction technique based on SOM and NSGA-II for gas turbine data pre-processing.

Persistent health monitoring of a gas turbine engine preserves the operational efficiency and lifetime of its components. An online monitoring system should meet requirements such as high accuracy, reliability against measurement ...

The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in ...

This paper presents a novel dimensionality reduction technique based on ELM and SOM: ELM-SOM+. This technique preserves the intrinsic quality of Self-Organizing Map (SOM): it is nonlinear and suitable for big data. It also brings ...

The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in ...

This paper addresses several topics of great interest in computer security in recent years: computer users’ behavior, security incidents and fraud exposure on the Internet, due to their high economic and social cost. Traditional ...

• We propose a Decision Support System (DSS) based on ontology and self-organising maps.

Analysis of warranty based big data has gained considerable attention due to its potential for improving the quality of products whilst minimizing warranty costs. Similarly, customer feedback information and warranty claims, which are ...

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. Here in this article, the authors introduce a new quality measure called the convergence index. The convergence index is a linear combination of map ...

This article describes how the objective of recent advances in soft computing and machine learning models is the resolution of issues related to security monitoring for information systems. Most current techniques and models face significant limitations,...

In this paper, we focus on the topic of an efficient initialization of neuron weights, which is one of key problems in artificial neural networks (ANNs). This problem is important in ANNs implemented as Application Specific Integrated Circuits (ASICs), ...

An approach for automatic classification and evaluation method for the structures of public World Wide Web sites by Naive-Bayes and Two-layer Convolutional Neural Networks is proposed in this paper. The proposed method is also worthy for analyzing ...

A neural network platform for human action recognition by observation of kinematics is introduced.The system is made of two layer SOMs and a costume made supervised neural network.Input is preprocessed by coordinate transformation, rescaling, attention ...