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Chapter
Fast Perceptron Learning by Fuzzy Controlled Dynamic Adaptation of Network Parameters
Application of fuzzy control for obtaining better performance from conventional neural networks is a new area in the field of fuzzy-neural combined systems. Conventional backpropagation algorithm for example c...
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Chapter
Mining of Labeled Incomplete Data Using Fast Dimension Partitioning
Two Dimensional Partitioning Techniques are proposed in this paper for fast mining of labeled data with missing values. The first Dimensional Partitioning Technique (DPT1) generates a classifier model by the u...
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Article
Particle Swarm Optimisation for Protein Motif Discovery
In this paper, a modified particle swarm optimisation algorithm is proposed for protein sequence motif discovery. Protein sequences are represented as a chain of symbols and a protein sequence motif is a short...
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Chapter and Conference Paper
Semi-supervised Learning of Dynamic Self-Organising Maps
We present a semi-supervised learning method for the Growing Self-Organising Maps (GSOM) that allows fast visualisation of data class structure on the 2D network. Instead of discarding data with missing values...
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Chapter and Conference Paper
Scalable Dynamic Self-Organising Maps for Mining Massive Textual Data
Traditional text clustering methods require enormous computing resources, which make them inappropriate for processing large scale data collections. In this paper we present a clustering method based on the wo...
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Chapter and Conference Paper
Combining News and Technical Indicators in Daily Stock Price Trends Prediction
Stock market prediction has always been one of the hottest topics in research, as well as a great challenge due to its complex and volatile nature. However, most of the existing methods neglect the impact from...
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Article
Open AccessGene function prediction based on genomic context clustering and discriminative learning: an application to bacteriophages
Existing methods for whole-genome comparisons require prior knowledge of related species and provide little automation in the function prediction process. Bacteriophage genomes are an example that cannot be ea...
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Chapter and Conference Paper
Information Theoretic Classification of Problems for Metaheuristics
This paper proposes a model for metaheuristic research which recognises the need to match algorithms to problems. An empirical approach to producing a map** from problems to algorithms is presented. This map...
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Article
Polynomial kernel adaptation and extensions to the SVM classifier learning
Three extensions to the Kernel-AdaTron training algorithm for Support Vector Machine classifier learning are presented. These extensions allow the trained classifier to adhere more closely to the constraints i...
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Article
Open AccessGenome classification by gene distribution: An overlap** subspace clustering approach
Genomes of lower organisms have been observed with a large amount of horizontal gene transfers, which cause difficulties in their evolutionary study. Bacteriophage genomes are a typical example. One recent app...
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Article
Open AccessBinning sequences using very sparse labels within a metagenome
In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, k-me...
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Chapter
The Rationale Behind Seeking Inspiration from Nature
There are currently numerous heuristic algorithms for combinatorial optimisation problems which are commonly described as nature-inspired. Parallels can certainly be drawn between these algorithms and various ...
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Chapter
Dynamic Self-Organising Maps: Theory, Methods and Applications
In an effort to counter the restrictions enforced by the fixed map size and aspect ratio of a Kohonen Self-Organising Map, many variants to the method have been proposed. As a recent development, the Dynamic S...
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Article
Structure adaptation of hierarchical knowledge-based classifiers
This paper introduces a new method to identify the qualified rule-relevant nodes to construct hierarchical neuro-fuzzy systems (HNFSs). After learning, the proposed method analyzes the entire history of activi...
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Article
Open AccessThe oligonucleotide frequency derived error gradient and its application to the binning of metagenome fragments
The characterisation, or binning, of metagenome fragments is an important first step to further downstream analysis of microbial consortia. Here, we propose a one-dimensional signature, OFDEG, derived from the...
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Chapter and Conference Paper
On the Task Specific Evaluation and Optimisation of Cable-Driven Manipulators
Cable-driven manipulators are traditionally designed for general performance objectives, such as maximisation of workspace. To take advantage of the reconfigurability of cable-driven mechanisms, the optimisati...
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Chapter and Conference Paper
A Meta-learning Prediction Model of Algorithm Performance for Continuous Optimization Problems
Algorithm selection and configuration is a challenging problem in the continuous optimization domain. An approach to tackle this problem is to develop a model that links landscape analysis measures and algorit...
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Chapter and Conference Paper
The Algorithm Selection Problem on the Continuous Optimization Domain
The problem of algorithm selection, that is identifying the most efficient algorithm for a given computational task, is non-trivial. Meta-learning techniques have been used successfully for this problem in par...
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Article
Open AccessCoNVEX: copy number variation estimation in exome sequencing data using HMM
One of the main types of genetic variations in cancer is Copy Number Variations (CNV). Whole exome sequenicng (WES) is a popular alternative to whole genome sequencing (WGS) to study disease specific genomic v...
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Article
Open AccessErratum to: CoNVEX: copy number variation estimation in exome sequencing data using HMM
