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

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

Data Mining Practical Machine Learning Tools and Techniques

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and know- ledge discovery. Proper classification of e-documents, online news, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing tech- niques to get meaningful knowledge. The aim of this paper is to highlight the important techniques and methodologies that are employed in text documents classification, while at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques. This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing litera- ture.

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A Review of Machine Learning Algorithms for Text-Documents Classification

Text categorization, which consists of automatically assigning documents to a set of categories, usually involves the management of a huge number of features. Most of them are irrelevant and others introduce noise which could mislead the classifiers. Thus, feature reduction is often performed in order to increase the efficiency and effectiveness of the classification. In this paper, we propose to select relevant features by means of a family of linear filtering measures which are simpler than the usual measures applied for this purpose. We carry out experiments over two different corpora and find that the proposed measures perform better than the existing ones.

Introducing a family of linear measures for feature selection in text categorization

High-performance computing (HPC) is nowadays an essential tool for the solution of many problems that arise in both scientific and engineering realms. HPC platforms are based on clusters of multicore nodes, and half of these facilities all around the world also include some type of accelerator device such as graphics processing units (GPUs) or the Intel Xeon Phi coprocessor. Many research interests are addressed to optimize applications that can get the most of these configurations. At the same time, research on the HPC ecosystem (hardware, software tools, applications, etc.) is in the spotlight. In particular, exascale computing is receiving a major interest. The White House Office of Science and Technology Policy highlights the importance of exascale computing for the maintenance of US leadership over the coming decades, and it is for this reason that the United States is doing strategic investments in HPC to meet increasing computing demands and emerging technological challenges. Current and future research faces the natural problems that arise when concurrent resources becomevery large: huge electrical consumption, heat dissipation, and probability of failure, among others. Many problems arise as long as we proceed in the way to developing exascale systems. One of them is the increase of failure rates. This special issue presents the

/pdf/high-performance-computing-the-essential-tool-and-the-hzy6fixsa4.pdf

High-performance computing: the essential tool and the essential challenge

In this paper we advocate the application of Artificial Intelligence techniques to quality assessment of food products. Machine Learning algorithms can help us to: (a) extract operative human knowledge from a set of examples; (b) conclude interpretable rules for classifying samples regardless of the non-linearity of the human behaviour or process; and (c) help us to ascertain the degree of influence of each objective attribute of the assessed food on the final decision of an expert. We illustrate these topics with an example of how it is possible to clone the behaviour of bovine carcass classifiers, leading to possible further industrial applications.

/pdf/the-usefulness-of-artificial-intelligence-techniques-to-v6s7rfdl0c.pdf

The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry

We propose a set of (machine learning) ML-based scoring measures for conducting feature selection. We've tested these measures on documents from two well-known corpora, comparing them with other measures previously applied for this purpose. In particular, we've analyzed which measure obtains the best overall classification performance in terms of properties such as precision and recall, emphasizing to what extent some statistical properties of the corpus affects performance. The results show that some of our measures outperform the traditional measures in certain situations.

Scoring and selecting terms for text categorization

In this paper we present a machine-learning algorithm that computes a small set of accurate and interpretable rules. The decisions of these rules can be straight-forwardly explained as the conclusions drawn by a case-based reasoner. Our system is named F AN, an acronym for f inding a ccurate i n ductions. It starts from a collection of training examples and produces propositional rules able to classify unseen cases following a minimum-distance criterion in their evaluation procedure. In this way, we combine the advantages of instance-based algorithms and the conciseness of rule (or decision-tree) inducers. The algorithm followed by F AN can be seen as the result of successive steps of pruning heuristics. The main tool employed is that of the impurity level, a measure of the classification quality of a rule, inspired by a similar measure used in IB3. Finally, a number of experiments were conducted with standard benchmark datasets of the UCI repository to test the performance of our system, successfully comparing F AN with a wide collection of machine-learning algorithms.

José Ranilla

Papers

Introducing a family of linear measures for feature selection in text categorization

High-performance computing: the essential tool and the essential challenge

The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry

Scoring and selecting terms for text categorization

F an