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.

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Opinion Mining and Sentiment Analysis

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.

Photo tourism: exploring photo collections in 3D

We propose a new browsing system called "Web2Talkshow". It transforms declarative-based web content into humorous dialog-based TV-program-like content that is presented through cartoon animation and synthesized speech. The system does this based on keywords in the original web content. Web2Talkshow enable users to get desired web content easily, pleasantly, and in a user-friendly way while being able to continue working on other tasks. Thus, using it will be much like watching TV.

Web2Talkshow: transforming web content into TV-program-like content based on the creation of dialogue

Most conventional database systems and information retrieval systems force users to specify the qualification conditions in queries in an ”absolute” manner. That is, a user must specify a qualification condition to be satisfied by each retrieval result. To do this properly, users must have sufficient knowledge about the metadata and their structures. In the real world, however, people often specify a qualification in a relative manner such as ”I prefer this one among these.” In this paper, we propose the notion of ”relative queries,” and their query processing method called the ”relative cluster-mapping.” A relative query specifies user’s selection criteria as selecting his/her favorite data among a sample data cluster. This notion is useful when users do not have sufficient knowledge about metadata, or users cannot decide a complete qualification condition. The ”relative cluster-mapping” method maps the relative position of the user-selected data in a sample data cluster to a target data cluster and returns an answer from the target data cluster. The answer‘s relative position in the target data cluster is similar to that of the selected data in a sample data cluster. We believe it is more natural to express the desired data using relative qualifications. We also have developed prototype system, and evaluated its appropriateness.

Relative Queries and the Relative Cluster-Mapping Method

Page revisiting is a popular browsing activity in the Web. In this paper we describe a method for improving page revisiting by detecting and highlighting the information on browsed Web pages that is fresh for a user. Content freshness is determined based on comparison with the previously viewed versions of pages. Any new content for the user is marked, enabling the user to quickly spot it. We also describe a mechanism for visually informing users about the degree of freshness of linked pages. By indicating the freshness level of content on linked pages, the system enables users to navigate the Web more effectively. Finally, we propose and demonstrate the concept of determining user-dependent, subjective age of page contents. Using this method, elements of Web pages are annotated with dates indicating the first time the elements were accessed by the user.

Personalized detection of fresh content and temporal annotation for improved page revisiting

Although the Web lets users freely browse and publish information, most Web information is unauthorized in contrast to conventional mass media. Therefore, it is not always credible or correct. We propose a model to solve these problems by enabling the credibility of text-image pairs on the Web to be analyzed. We propose a bipartite graph model, in which one set of nodes corresponds to a set of text data, and the other corresponds to a set of images. That is, each text-image pair is represented by an edge. We introduce the notion of "supportive relationships" among edges in our bipartite graph model. Intuitively, our hypothesis is that the more "supportive" text-image pairs a target text-image pair has, the more credible it is. Although such a bipartite graph model is by itself not necessarily new, one of its most notable features is that we take into consideration the similarity (or dissimilarity) among nodes in each node set to compute supportive relationships. As our model is generic, it can be applied to a variety of types of Web information represented by text-image pairs. We especially focus on the analysis of text-image pairs on the Web in this paper and describe a practical implementation of our model for analyzing credibility, ImageAlert.

ImageAlert: credibility analysis of text-image pairs on the web

In recent years, we have witnessed a rapid increase of text con- tent stored in digital archives such as newspaper archives or web archives. Many old documents have been converted to digital form and made accessible online. Due to the passage of time, it is however difficult to effectively perform search within such collections. Users, especially younger ones, may have problems in finding appropriate keywords to perform effective search due to the terminology gap arising between their knowledge and the unfamiliar domain of archival collections. In this paper, we provide a general framework to bridge different domains across-time and, by this, to facilitate search and comparison as if carried in user's familiar domain (i.e., the present). In particular, we propose to find analogical terms across temporal text collections by applying a series of transformation procedures. We develop a cluster-biased transformation technique which makes use of hierarchical cluster structures built on the temporally distributed document collections. Our methods do not need any specially prepared training data and can be applied to diverse collections and time periods. We test the performance of the proposed approaches on the collections separated by both short (e.g., 20 years) and long time gaps (70 years), and we report improvements in range of 18%-27% over short and 56%-92% over long periods when compared to state-of-the-art baselines.

Katsumi Tanaka

Papers

Web2Talkshow: transforming web content into TV-program-like content based on the creation of dialogue

Relative Queries and the Relative Cluster-Mapping Method

Personalized detection of fresh content and temporal annotation for improved page revisiting

ImageAlert: credibility analysis of text-image pairs on the web

Temporal Analog Retrieval using Transformation over Dual Hierarchical Structures