When we deconstruct a complex problem we often find patterns among the smaller problems we create. The patterns are similarities or characteristics that some of the problems share. Pattern recognition is one of the four cornerstones of computer science. It involves finding the similarities or patterns among small, deconstruct problems that can help us solve more complex problems more efficiently. Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labelled “training” data (supervised learning), but when no labelled data are available other algorithms can be used to discover previously unknown patterns (unsupervised) learning.
Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in the coming years. Soft Computing encourages the integration of soft computing techniques and tools into both every day and advanced applications.
The primary aim of this special issue is to publish the highest quality research in application and convergence of the areas of fuzzy logic, neural networks, evolutionary computing, rough sets and other similar techniques to address real-world complexities with a focus on pattern recognition and machine learning.
The issue will carry revised and substantially extended versions of selected papers presented at the Third International Conference on Knowledge-Based Engineering and Innovation (KBEI-2016), but we also strongly encourage researchers unable to participate in the conference to submit articles for this call.
Suitable topics include, but are not limited to, the following:
- Statistical, syntactic and structural pattern recognition
- Pattern recognition in bioinformatics and biologic computations
- Pattern recognition for big data
- Classification and clustering
- Graphical models for pattern recognition
- Support vector machines and kernel methods
- Pattern recognition using revolutionary optimisation methods
- Pattern recognition in signal processing
- Dimensionality reduction
- Deep learning
- Transfer learning
- Fuzzy and hybrid techniques in pattern recognition
- Natural language processing and recognition
- Feature extraction, selection and enhancement methods
- Machine learning and data mining
- Evolutionary computing
- Software agents
- Morphic computing
- Probabilistic reasoning
- Rough sets
- Image and video processing
- Sparse theory
- Blind source separation
- Medical signal processing
Submission of manuscripts: 20 March, 2017