An Open Source Pattern Recognition Toolbox for MATLAB

Pattern recognition and machine learning are becoming integral parts of algorithms in a wide range of applications. Different algorithms and approaches for machine learning include different tradeoffs between performance and computation, so during algorithm development it is often necessary to explore a variety of different approaches to a given task. A toolbox with a unified framework across multiple pattern recognition techniques enables algorithm developers the ability to rapidly evaluate different choices prior to deployment. MATLAB is a widely used environment for algorithm development and prototyping, and although several MATLAB toolboxes for pattern recognition are currently available these are either incomplete, expensive, or restrictively licensed. In this work we describe a MATLAB toolbox for pattern recognition and machine learning known as the PRT (Pattern Recognition Toolbox), licensed under the permissive MIT license. The PRT includes many popular techniques for data preprocessing, supervised learning, clustering, regression and feature selection, as well as a methodology for combining these components using a simple, uniform syntax. The resulting algorithms can be evaluated using cross-validation and a variety of scoring metrics to ensure robust performance when the algorithm is deployed. This paper presents an overview of the PRT as well as an example of usage on Fisher's Iris dataset

Machine Learning and Pattern Recognition

Learning is needed when there is no human expertise existing or when human beings are unable to explain their expertise. In such a situation, one simply collects all the possible previous information, analyse it and then make a rule for future prediction or taking meaningful decision. When we plan to conclude such a work with the help of a computer by providing it ample amount of data and our past experience with tools and techniques, then the whole process becomes machine learning. Hence, machine learning can be defined as programming computers to optimise a performance criterion using example data and past experience. For example, recognition of spoken speech is being done by human beings seemingly without any difficulty, but cannot explain how they do it.Defence Science Journal, 2010, 60(4), pp.345-347, DOI:http://dx.doi.org/10.14429/dsj.60.50

PATTERN RECOGNITION AND MACHINE LEARNING

Model Credit is such an issue while Machine Learning is a grouping of plan. Model affirmation is sturdily associated with Artificial Intelligence and Machine Learning. Model Recognition is an organizing practice of Machine Learning. Fake keenness contract with turn of events and assessment of systems that have the choice to pick up from data, somewhat than stick to just obviously changed principles while Pattern demand is the validation of models and surface in estimations maybe the most noteworthy vocations of learning are graph insistence. Proposed measure that utilizes each around coordinated appraisals notice typical world in photos, inconsistencies in crowd differentiations, and signs of peril in mammograms for improved than individual

Proof-Pattern Recognition and Lemma Discovery in ACL2

We present a novel technique for combining statistical machine learning for proof-pattern recognition with symbolic methods for lemma discovery. The resulting tool, ACL2(ml), gathers proof statistics and uses statistical pattern-recognition to pre-processes data from libraries, and then suggests auxiliary lemmas in new proofs by analogy with already seen examples. This paper presents the implementation of ACL2(ml) alongside theoretical descriptions of the proof-pattern recognition and lemma discovery methods involved in it

Lasso based feature selection for malaria risk exposure prediction

In life sciences, the experts generally use empirical knowledge to recode variables, choose interactions and perform selection by classical approach. The aim of this work is to perform automatic learning algorithm for variables selection which can lead to know if experts can be help in they decision or simply replaced by the machine and improve they knowledge and results. The Lasso method can detect the optimal subset of variables for estimation and prediction under some conditions. In this paper, we propose a novel approach which uses automatically all variables available and all interactions. By a double cross-validation combine with Lasso, we select a best subset of variables and with GLM through a simple cross-validation perform predictions. The algorithm assures the stability and the the consistency of estimators.Comment: in Petra Perner. Machine Learning and Data Mining in Pattern Recognition, Jul 2015, Hamburg, Germany. Ibai publishing, 2015, Machine Learning and Data Mining in Pattern Recognition (proceedings of 11th International Conference, MLDM 2015

Machine Learning in Image Analysis and Pattern Recognition

This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition