There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

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Survey of clustering algorithms

This work describes algorithms for the inference of minimum size deterministic automata consistent with a labeled training set. The algorithms presented represent the state of the art for this problem, known to be computationally very hard.

In particular, we analyze the performance of algorithms that use implicit enumeration of solutions and algorithms that perform explicit search but incorporate a set of techniques known as dependency directed backtracking to prune the search tree effectively.

We present empirical results that show the comparative efficiency of the methods studied and discuss alternative approaches to this problem, evaluating their advantages and drawbacks.

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Efficient Algorithms for the Inference of Minimum Size DFAs

Increased application of structured pattern mining requires a perfect understanding of the problem and a clear identification of the advantages and disadvantages of existing algorithms. Among those algorithms, pattern-growth methods have been shown to have the best performance when applied to sequential pattern mining. However, their advantages over apriori-based methods are not well explained and understood. Detailed analysis of the performance and memory requirements for these algorithms shows that counting the support for each potential pattern is the most computationally demanding step. Additionally, the analysis makes clear that the main advantage of patterngrowth over apriori-based methods resides on the restriction of the search space that is obtained from the creation of projected databases. In this paper, we present this analysis and describe how apriori-based algorithms can achieve the efficiency of pattern-growth methods.

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Sequential Pattern Mining Algorithms: Trade-offs between Speed and Memory

In this paper we explore the potential of using a general class of functional representation techniques, kernel-based regression, in the nonlinear model reduction problem. The kernel-based viewpoint provides a convenient computational framework for regression, unifying and extending the previously proposed polynomial and piecewise-linear reduction methods. Furthermore, as many familiar methods for linear system manipulation can be leveraged in a nonlinear context, kernels provide insight into how new, more powerful, nonlinear modeling strategies can be constructed. We present an SVD-like technique for automatic compression of nonlinear models that allows systematic identification of model redundancies and rigorous control of approximation error.

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Analog Macromodeling using Kernel Methods

In this paper we study the effect of using unlabeled data in conjunction with a small portion of labeled data on the accuracy of a centroid-based classifier used to perform single-label text categorization. We chose to use centroid-based methods because they are very fast when compared with other classification methods, but still present an accuracy close to that of the state-of-the-art methods. Efficiency is particularly important for very large domains, like regular news feeds, or the web.We propose the combination of Expectation-Maximization with a centroid-based method to incorporate information about the unlabeled data during the training phase. We also propose an alternative to EM, based on the incremental update of a centroid-based method with the unlabeled documents during the training phase.We show that these approaches can greatly improve accuracy relatively to a simple centroid-based method, in particular when there are very small amounts of labeled data available (as few as one single document per class).Using one synthetic and three real-world datasets, we show that, if the initial model of the data is sufficiently precise, using unlabeled data improves performance. On the other hand, using unlabeled data degrades performance if the initial model is not precise enough.

Semi-supervised single-label text categorization using centroid-based classifiers

The installation of software packages depends on the correct resolution of dependencies and conflicts between packages. This problem is NP-complete and, as expected, is a hard task. Moreover, today's technology still does not address this problem in an acceptable way. This paper introduces a new approach to solving the software dependency problem in a Linux environment, devising a way for solving dependencies according to available packages and user preferences. This work introduces the "apt-pbo" tool, the first publicly available tool that solves dependencies in a complete and optimal way.

/pdf/apt-pbo-solving-the-software-dependency-problem-using-pseudo-27ziktpu0w.pdf

Arlindo L. Oliveira

Papers

Efficient Algorithms for the Inference of Minimum Size DFAs

Sequential Pattern Mining Algorithms: Trade-offs between Speed and Memory

Analog Macromodeling using Kernel Methods

Semi-supervised single-label text categorization using centroid-based classifiers

Apt-pbo: solving the software dependency problem using pseudo-boolean optimization