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

Data Mining - Concepts and Techniques.

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

/pdf/opinion-mining-and-sentiment-analysis-3v5ya3qqiw.pdf

Opinion Mining and Sentiment Analysis

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering --- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.

Neural Collaborative Filtering

Cross-Device User Linking is the task of detecting same users given their browsing logs on different devices (e.g., tablet, mobile phone, PC, etc.). The problem was introduced in CIKM Cup 2016 together with a new dataset provided by Data-Centric Alliance (DCA). In this paper, we present insightful analysis on the dataset and propose a solution to link users based on their visited URLs, visiting time, and profile embeddings. We cast the problem as pairwise classification and use gradient boosting as the leaning-to-rank model. Our model works on a set of features exacted from URLs, titles, time and session data derived from user device-logs. The model outperforms the best solution in the CIKM Cup by a large margin.

Cross-Device User Linking: URL, Session, Visiting Time, and Device-log Embedding

Digital libraries have been rather successful in supporting learning activities by providing learners with access to information and knowledge. However, this level of support is passive to learners and interactive and collaborative learning cannot be easily achieved. In this paper, we study how digital libraries could be extended to serve a more active role in collaborative learning activities. We focus on developing new services to support a common type of learning activity, field study, in a geospatial context. We propose the concept of personal project space that allows individuals to work in their personalized environment with a mix of private and public data and at the same time to share part of the data with team members. To support the portability of the resources in our digital library, the selected resources can be exported in an organized manner.

/pdf/supporting-field-study-with-personalized-project-spaces-in-a-52091zd31m.pdf

Supporting field study with personalized project spaces in a geographical digital library

In web classi cation, web pages from one or more web sitesare assigned to pre-de nedcategories according to theircon-tent. Since web pages are more than just plain text docu-ments, web classi cation methods have to consider usingother context features of web pages, such as hyperlinks andHTML tags. In this paper, we propose the use of SupportVectorMachine (SVM)classi ers to classify web pagesusingboth their text and context feature sets. We have experi-mented our web classi cation method on the WebKB dataset. Compared with earlier Foil-Pilfs method on the samedata set, our method has been shown to perform very well.We have also shown that the use of context features espe-cially hyperlinks can improve the classi cation performancesigni cantly. Categories and Subject Descriptors H.3.3 [Information Systems]: Information Storage andRetrieval; H.2.8 [Database Management]: Database Ap-plications|Data mining General Terms Experimentation Keywords Web Classi cation, Web Mining, SVM 1. INTRODUCTION

Web Classication Using Support Vector Machine

With an explosive growth of blogs, information seeking in blogosphere becomes more and more challenging. One example task is to find the most relevant topical blogs against a given query or an existing blog. Such a task requires concise representation of blogs for effective and efficient searching and matching. In this paper, we investigate a new problem of profiling a blog by choosing a set of m most representative entries from the blog, where m is a predefined number that is application-dependent. With the set of selected representative entries, applications on blogs avoid handling hundreds or even thousands of entries (or posts) associated with each blog, which are updated frequently and often noisy in nature. To guide the process of selecting the most representative entries, we propose three principles, i.e., anomaly, representativeness, and diversity. Based on these principles, a greedy yet very efficient entry selection algorithm is proposed. To evaluate the entry selection algorithms, an extrinsic evaluation methodology from document summarization research is adapted. Specifically, we evaluate the proposed entry selection algorithms by examining their blog classification accuracies. By evaluating on a number of different classification methods, our empirical results showed that comparable classification accuracy could be achieved by using fewer than 20 representative entries for each blog compared to that of engaging all entries.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3405&context=sis_research

On profiling blogs with representative entries

Support Vector Machines (SVM) classifiers are widely used in text classification tasks and these tasks often involve imbalanced training. In this paper, we specifically address the cases where negative training documents significantly outnumber the positive ones. A generic algorithm known as FISA (Feature-based Instance Selection Algorithm), is proposed to select only a subset of negative training documents for training a SVM classifier. With a smaller carefully selected training set, a SVM classifier can be more efficiently trained while delivering comparable or better classification accuracy. In our experiments on the 20-Newsgroups dataset, using only 35% negative training examples and 60% learning time, methods based on FISA delivered much better classification accuracy than those methods using all negative training documents.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1893&context=sis_research

Aixin Sun

Papers

Cross-Device User Linking: URL, Session, Visiting Time, and Device-log Embedding

Supporting field study with personalized project spaces in a geographical digital library

Web Classication Using Support Vector Machine

On profiling blogs with representative entries

FISA: feature-based instance selection for imbalanced text classification