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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

Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

Overview Graphical models: basic definitions and applications. Inference and learning in credal networks. Quick break: software packages. Formal definitions and computational complexity. Applications. Later Exact and approximate algorithms for reasoning. Agenda Some motivation Graph-theoretical statistical models, directed and undirected Bayesian networks Markov random fields and other graph-theoretical models Credal networks Other models related to credal networks Learning and Reasoning Software packages Computational Complexity Final remarks Applications Computer vision problems Military planning What is this? Why spend time on such a topic? A credal network is a compact representation for a set of probability distributions. It is a based on graphs, so it is easy to draw and to understand. It offers a compact and easy language in which to express complicated situations. It is closely related to very popular statistical models such as Markov chains, Bayesian networks, Markov random fields, etc. Compact and easy language to represent uncertainty. Network developed by the IDSIA team. Goal: to support decision making regarding such flows. (c) Segmentation produced by Bayesian network (d) Segmentation produced by credal network And an application on knowledge representation Description logics are used to create terminologies. These sentences encode a large credal network. Agenda Some motivation Graph-theoretical statistical models, directed and undirected Bayesian networks Markov random fields and other graph-theoretical models Credal networks Other models related to credal networks Learning and Reasoning Software packages Computational Complexity Final remarks Applications Computer vision problems Military planning First, the unstructured approach We can compute the probability of events by marginalization: Inferences Conditional probabilities are obtained by Bayes rule: Drawbacks: Exponential number of parameters to elicit. Exponential number of terms in the summation to perform inferences. Bayesian networks, Markov random fields, etc, provide a way to alleviate these issues through graph-theoretical tools.

Credal networks

Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in two applications related to human-computer interaction and pattern recognition: facial expression recognition and face detection.

/pdf/semisupervised-learning-of-classifiers-theory-algorithms-and-1m191xmbv6.pdf

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

This paper analyzes the performance of semi-supervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations where additional labeled data would decrease classification error. We present a mathematical analysis of this "degradation" phenomenon and show that it is due to the fact that bias may be adversely affected by unlabeled data. We discuss the impact of these theoretical results to practical situations.

/pdf/semi-supervised-learning-of-mixture-models-4ig8fpr08y.pdf

Semi-supervised learning of mixture models

Light power is affected when it crosses the atmosphere; there is a simple, albeit non-linear, relationship between the radiance of an image at any given wavelength and the distance between object and viewer This phenomenon is called atmospheric scattering and has been extensively studied by physicists and meteorologists We present the first analysis of this phenomenon from an image understanding perspective: we investigate a group of techniques for extraction of depth cues solely from the analysis of atmospheric scattering effects in images Depth from scattering techniques are discussed for indoor and outdoor environments, and experimental tests with real images are presented We have found that depth cues in outdoor scenes can be recovered with surprising accuracy and can be used as an additional information source for autonomous vehicles

Depth from scattering

This paper analyzes the effect of unlabeled training data in generative classifiers. We are interested in classification performance when unlabeled data are added to an existing pool of labeled data. We show that unlabeled data can degrade the performance of a classifier when there are discrepancies between modeling assumptions used to build the classifier and the actual model that generates the data; our analysis of this situation explains several seemingly disparate results in the literature.

/pdf/unlabeled-data-can-degrade-classification-performance-of-2xk3bssxsp.pdf

Fabio Gagliardi Cozman

Papers

Credal networks

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

Semi-supervised learning of mixture models

Depth from scattering

Unlabeled Data Can Degrade Classification Performance of Generative Classifiers