Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A survey on deep learning in medical image analysis

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants.

Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization

Within artificial intelligence and machine learning, a generative model is a powerful tool for learning any kind of data distribution. With the advent of deep learning and its success in image recognition, the field of deep generative models has clearly emerged as one of the promising fields for medical imaging. In a recent issue of The Journal of Pathology, Levine, Peng et al demonstrate the ability of generative models to synthesize high-quality pathology images. They suggested that generative models can serve as an unlimited source of images either for educating freshman pathologists or training machine learning models for diverse image analysis tasks, especially in scarce cases, while resolving patients' privacy and confidentiality concerns. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/path.5577

Generative models in pathology: synthesis of diagnostic quality pathology images†.

Encoded Local Projections (ELP) is a recently introduced dense sampling image descriptor which uses projections in small neighbourhoods to construct a histogram/descriptor for the entire image. ELP has shown to be as accurate as other state-of-the-art features in searching medical images while being time and resource efficient. This paper attempts for the first time to utilize ELP descriptor as primary features for facial recognition and compare the results with LBP histogram on the Labeled Faces in the Wild dataset. We have evaluated descriptors by comparing the chi-squared distance of each image descriptor versus all others as well as training Support Vector Machines (SVM) with each feature vector. In both cases, the results of ELP were better than LBP in the same sub-image configuration.

Facial Recognition with Encoded Local Projections

Segmentierung ist eine fundamentale Aufgabe der Bild Verarbeitung. Kaum eine Aufgabenstellung kann ohne Segmentierung, sei es Pixelklassifikation, Binarisierung oder Kantendetektion, gelost werden (die letztere Aufgabe hat sich in den letzen Jahren zu einem eigenen Bereich entwickelt, zahlt jedoch ebenfalls zu segmentierenden Verfahren). Wir konzentrieren uns hier auf die Binarisierung, weil die Behandlung der Segmentierung ohne diese Beschrankung den Rahmen dieses Beitrags sprengen wurde. Binarisierung, als die einfachste Form der Bildsegmentierung, ist eine der kritischsten Schritte der Bild Verarbeitung. Obwohl sie eine relativ einfache Klassifikationsaufgabe darstellt (namlich die Pixel entweder dem Hintergrund oder dem Objekt zuzuordnen), ist dieser Schritt mit diversen Problemen und Unsicherheiten behaftet. Oft last sich keine eindeutige bzw. klare Grenze zwischen Objekt und Hintergrund ziehen. Dies ist vor allem auf die inharente Unscharfe digitaler Bilder zuruckzufuhren. Dies hat zur Folge, das das Ergebnis der Binarisierung oft nicht optimal ist; eine eindeutige Trennung zwischen Objekt und Hintergrund kann nicht herbeigefuhrt werden (Abbildung 1). In der Praxis wird dies durch Storfaktoren wie ungleichmasige Beleuchtung, Reflexionen an der Objektoberflache, Schmutzpartikel usw. verstarkt.

Über Binarisierung und Potentiale der Fuzzy-Ansätze

Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE). A small population size decreases the computational complexity but also reduces the exploration ability of DE by limiting the population diversity. In this paper, we propose the idea of combining ensemble mutation scheme selection and opposition-based learning concepts to enhance the diversity of population in MDE at mutation and selection stages. The proposed algorithm enhances the diversity of population by generating a random mutation scale factor per individual and per dimension, randomly assigning a mutation scheme to each individual in each generation, and diversifying individuals selection using opposition-based learning. This approach is easy to implement and does not require the setting of mutation scheme selection and mutation scale factor. Experimental results are conducted for a variety of objective functions with low and high dimensionality on the CEC BlackBox Optimization Benchmarking 2015 (CEC-BBOB 2015). The results show superior performance of the proposed algorithm compared to the other micro-DE algorithms.

Opposition-based ensemble micro-differential evolution

A semi-automated object segmentation approach has been introduced in this paper. Object segmentation is a crucial task in image processing. The proposed approach learns segmentation from a small number of gold samples. The segmentation is performed in two main sequential steps, namely, target object localization, by applying optimal mathematical morphology procedure, and segmentation, by conducting some basic image processing operations. The outstanding feature of this approach is, unlike other existent approaches, that it does not need a prior knowledge or a large number of samples to learn from. The performance of the approach has been examined by a comprehensive well-designed validation set. For all test images, the target object was segmented accurately and the conducted experiments clearly showed that the proposed segmentation approach is highly invariant to noise, rotation, translation, overlapping, and scaling. The architecture of the approach and employed methodologies are explained in detail. Results are provided.

http://www.wseas.us/e-library/conferences/2005corfu/c1/papers/498-483.pdf

Hamid R. Tizhoosh

Papers

Generative models in pathology: synthesis of diagnostic quality pathology images†.

Facial Recognition with Encoded Local Projections

Über Binarisierung und Potentiale der Fuzzy-Ansätze

Opposition-based ensemble micro-differential evolution

Robust object segmentation using genetic optimization of morphological processing chains