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

CA : A Cancer Journal for Clinicians

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Content-based retrieval of medical images has been shown to be useful for various applications. However, as images are typically stored in compressed form, a certain overhead in terms of computational cost, required bandwith and storage resources is associated with the retrieval process. In this paper we show that by performing retrieval directly in the compressed domain of the images this can be avoided. We employ a wavelet-based compression approach and make direct use of the derived wavelet coefficients for the calculation of image similarity and hence retrieval. Experiments performed on a database of 400+ medical infrared images show that our approach supports good retrieval performance.

Content-based retrieval of compressed medical images

This paper compares the performance of various rule-based classification systems. In the classification problems in this paper it is assumed that a misclassification cost is associated with each training pattern. Thus, the task of classification is to minimize the total sum of misclassification costs rather than to maximize the classification rate. Ifthen rules are being generated from a given set of training patterns. The differences between the classification systems used in this paper are (a) whether fuzzy sets or interval sets are used in the antecedent part of ifthen rules, and (b) how the consequent part of the if-then rules is determined. In the determination of the consequent part of if-then rules we consider cost-based and compatibility-based determination . In costbased determination, the consequent class of a rule is determined so that the misclassification costs is minimal over the covered training patterns by the antecedent part of the rule. On the other hand, in compatibilitybased determination the consequent class of an if-then rule is determined from the compatibility of training patterns covered by the antecedent part of the rule. The grade of certainty of the rules in both determination types is calculated by using the compatibility of training patterns from each class. In a series of computational experiments, we examine the performance of the classification systems for three real-world pattern classification problems.

/pdf/generating-classification-rules-from-numerical-data-with-2e6rjzyps5.pdf

Generating Classification Rules From Numerical Data With Misclassification Cost

Multi-Scale Feature Fusion for Single Image Novel View Synthesis

Interest in microarray studies and gene expression analysis is growing as they are likely to provide promising avenues towards the understanding of fundamental questions in biology and medicine. In this paper we employment a hybrid fuzzy rule-based classification system for effective analysis of gene expression data. Our classifier consists of a set of fuzzy if-then rules that allows for accurate non-linear classification of input patterns. A small number of fuzzy if-then rules are selected through means of a genetic algorithm in order to provide a compact classifier for gene expression analysis. Experimental results on various well-known gene expression datasets confirm the efficacy of the presented approach.

Gerald Schaefer

Papers

Content-based retrieval of compressed medical images

Generating Classification Rules From Numerical Data With Misclassification Cost

Multi-Scale Feature Fusion for Single Image Novel View Synthesis

Michigan style fuzzy classification for gene expression analysis

Distributed 3-D Medical Image Registration Using Intelligent Agents