LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

/pdf/libsvm-a-library-for-support-vector-machines-2gdxasg9zv.pdf

LIBSVM: A library for support vector machines

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

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

/pdf/data-mining-concepts-and-techniques-4dtvdfkvmi.pdf

Data Mining: Concepts and Techniques

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

/pdf/a-tutorial-on-support-vector-machines-for-pattern-5cn6x20psd.pdf

A Tutorial on Support Vector Machines for Pattern Recognition

Social Media offer a vast amount of geo-located and time-stamped textual content directly generated by people. This information can be analysed to obtain insights about the general state of a large population of users and to address scientific questions from a diversity of disciplines. In this work, we estimate temporal patterns of mood variation through the use of emotionally loaded words contained in Twitter messages, possibly reflecting underlying circadian and seasonal rhythms in the mood of the users. We present a method for computing mood scores from text using affective word taxonomies, and apply it to millions of tweets collected in the United Kingdom during the seasons of summer and winter. Our analysis results in the detection of strong and statistically significant circadian patterns for all the investigated mood types. Seasonal variation does not seem to register any important divergence in the signals, but a periodic oscillation within a 24-hour period is identified for each mood type. The main common characteristic for all emotions is their mid-morning peak, however their mood score patterns differ in the evenings.

Analysing Mood Patterns in the United Kingdom through Twitter Content

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a “protected concept”, that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of “being right for the right reason”, where we request a classifier to make a decision in a way that is entirely ‘agnostic’ to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an ‘agnostic model’, we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.

Right for the Right Reason: Training Agnostic Networks

The psychological state of a person is characterised by cognitive and emotional variables which can be inferred by psychometric methods. Using the word lists from the Linguistic Inquiry and Word Count, designed to infer a range of psychological states from the word usage of a person, we studied temporal changes in the average expression of psychological traits in the general population. We sampled the contents of Twitter in the United Kingdom at hourly intervals for a period of four years, revealing a strong diurnal rhythm in most of the psychometric variables, and finding that two independent factors can explain 85% of the variance across their 24-h profiles. The first has peak expression time starting at 5am/6am, it correlates with measures of analytical thinking, with the language of drive (e.g power, and achievement), and personal concerns. It is anticorrelated with the language of negative affect and social concerns. The second factor has peak expression time starting at 3am/4am, it correlates with the language of existential concerns, and anticorrelates with expression of positive emotions. Overall, we see strong evidence that our language changes dramatically between night and day, reflecting changes in our concerns and underlying cognitive and emotional processes. These shifts occur at times associated with major changes in neural activity and hormonal levels.

/pdf/diurnal-variations-of-psychometric-indicators-in-twitter-2u2xuk7krz.pdf

Diurnal variations of psychometric indicators in Twitter content

We explore the problem of learning to predict the popularity of an article in online news media. By "popular" we mean an article that was among the "most read" articles of a given day in the news outlet that published it. We show that this cannot be modelled simply as the binary classification task of separating popular from unpopular articles, thereby assuming that popularity is an absolute property. Instead, we propose to view popularity in the perspective of a competitive situation where the popular articles are those which were the most appealing on that particular day. This leads to the notion of an "appeal" function, to model which we use a linear function in the bag of words representation. The parameters of this linear function are learnt from a training set formed by pairs of documents, one of which was popular and the other which appeared on the same page and date, without becoming popular. To learn the appeal function we use Ranking Support Vector Machines, using data collected from six different outlets over a period of 1 year. We show that our method can predict which articles will become popular, as well as extracting those keywords that mostly affect the appeal function. This also enables us to compare different outlets from the point of view of their readers' preference patterns. Remarkably, this is achieved using very limited information, namely the textual content of title and description of each article, the page and date of publication, and whether it became popular.

Modelling and predicting news popularity

We present a method for the classification of multi-labeled text documents explicitly designed for data stream applications that require to process a virtually infinite sequence of data using constant memory and constant processing time. Our method is composed of an online procedure used to efficiently map text into a low-dimensional feature space and a partition of this space into a set of regions for which the system extracts and keeps statistics used to predict multi-label text annotations. Documents are fed into the system as a sequence of words, mapped to a region of the partition, and annotated using the statistics computed from the labeled instances colliding in the same region. This approach is referred to as clashing. We illustrate the method in real-world text data, comparing the results with those obtained using other text classifiers. In addition, we provide an analysis about the effect of the representation space dimensionality on the predictive performance of the system. Our results show that the online embedding indeed approximates the geometry of the full corpus-wise TF and TF-IDF space. The model obtains competitive F measures with respect to the most accurate methods, using significantly fewer computational resources. In addition, the method achieves a higher macro-averaged F measure than methods with similar running time. Furthermore, the system is able to learn faster than the other methods from partially labeled streams.

Nello Cristianini

Papers

Analysing Mood Patterns in the United Kingdom through Twitter Content

Right for the Right Reason: Training Agnostic Networks

Diurnal variations of psychometric indicators in Twitter content

Modelling and predicting news popularity

Efficient classification of multi-labeled text streams by clashing