More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.

/pdf/the-weka-data-mining-software-an-update-3zs9onav7o.pdf

The WEKA data mining software: an update

Data Mining - Concepts and Techniques.

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

From the Publisher: 
A Question of Identity 
Life on the Screen is a fascinating and wide-ranging investigation of the impact of computers and networking on society, peoples' perceptions of themselves, and the individual's relationship to machines. Sherry Turkle, a Professor of the Sociology of Science at MIT and a licensed psychologist, uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, "bots," virtual reality, and "the on-line way of life." 
Turkle's discussion of postmodernism is particularly enlightening. She shows how postmodern concepts in art, architecture, and ethics are related to concrete topics much closer to home, for example AI research (Minsky's "Society of Mind") and even MUDs (exemplified by students with X-window terminals who are doing homework in one window and simultaneously playing out several different roles in the same MUD in other windows). Those of you who have (like me) been turned off by the shallow, pretentious, meaningless paintings and sculptures that litter our museums of modern art may have a different perspective after hearing what Turkle has to say. 
This is a psychoanalytical book, not a technical one. However, software developers and engineers will find it highly accessible because of the depth of the author's technical understanding and credibility. Unlike most other authors in this genre, Turkle does not constantly jar the technically-literate reader with blatant errors or bogus assertions about how things work. Although I personally don't have time or patience for MUDs,view most of AI as snake-oil, and abhor postmodern architecture, I thought the time spent reading this book was an extremely good investment.

스크린 위의 삶 = Life on the screen : identity in the age of the internet

KDD is a complex and demanding task While a large number of methods has been established for numerous problems, many challenges remain to be solved New tasks emerge requiring the development of new methods or processing schemes Like in software development, the development of such solutions demands for careful analysis, specification, implementation, and testing Rapid prototyping is an approach which allows crucial design decisions as early as possible A rapid prototyping system should support maximal re-use and innovative combinations of existing methods, as well as simple and quick integration of new onesThis paper describes Yale, a free open-source environment forKDD and machine learning Yale provides a rich variety of methods whichallows rapid prototyping for new applications and makes costlyre-implementations unnecessary Additionally, Yale offers extensive functionality for process evaluation and optimization which is a crucial property for any KDD rapid prototyping tool Following the paradigm of visual programming eases the design of processing schemes While the graphical user interface supports interactive design, the underlying XML representation enables automated applications after the prototyping phaseAfter a discussion of the key concepts of Yale, we illustrate the advantages of rapid prototyping for KDD on case studies ranging from data pre-processing to result visualization These case studies cover tasks like feature engineering, text mining, data stream mining and tracking drifting concepts, ensemble methods and distributed data mining This variety of applications is also reflected in a broad user base, we counted more than 40,000 downloads during the last twelve months

YALE: rapid prototyping for complex data mining tasks

We present a freely available benchmark dataset for audio classification and clustering. This dataset consists of 10 seconds samples of 1886 songs obtained from the Garageband site. Beside the audio clips themselves, textual meta data is provided for the individual songs. The songs are classified into 9 genres. In addition to the genre information, our dataset also consists of 24 hierarchical cluste r models created manually by a group of users. This enables a user centric evaluation of audio classification and clustering algorithms and gives researchers the opportunity to test the performance of their methods on heterogeneous data. We first give a motivation for assembling our benchmark dataset. Then we describe the dataset and its elements in more detail. Finally, we present some initial results using a set of audio features generated by a feature construction approach.

/pdf/a-benchmark-dataset-for-audio-classification-and-clustering-2u80pw3u6a.pdf

A benchmark dataset for audio classification and clustering

Knowledge exchange between heterogeneous communities of practice has been recognized as the critical source of innovation and creation of new knowledge. This paper considers the problem of enabling such cross community knowledge exchange through knowledge visualization. We discuss the social nature of knowledge construction and describe main requirements for practical solutions to the given problem, as well as existing approaches. Based on this analysis, we propose a model for collaborative elicitation and visualization of community knowledge perspectives based on the construction of personalised learning knowledge maps and shared concept networks that incorporate implicit knowledge and personal views of individual users. We show how this model supports explicit and implicit exchange of knowledge between the members of different communities and present its prototypical realization in the Knowledge Explorer, an interactive tool for collaborative visualization and cross-community sharing of knowledge. Concrete application scenarios and evaluation experiences are discussed on the example of the Internet platform netzspannung.org.

/pdf/collaborative-knowledge-visualization-for-cross-community-4j9fdyjxon.pdf

Collaborative knowledge visualization for cross-community learning

This paper presents an agent-based approach to semantic exploration and knowledge discovery in large information spaces by means of capturing, visualizing and making usable implicit knowledge structures of a group of users. The focus is on the developed conceptual model and system for creation and collaborative use of personalized learning knowledge maps. We use the paradigm of agents on the one hand as model for our approach, on the other hand it serves as a basis for an efficient implementation of the system. We present an unobtrusive model for profiling personalised user agents based on two dimensional semantic maps that provide 1) a medium of implicit communication between human users and the agents, 2) form of visual representation of resulting knowledge structures. Concerning the issues of implementation we present an agent architecture, consisting of two sets of asynchronously operating agents, which enables both sophisticated processing, as well as short respond times necessary for enabling interactive use in real-time.

/pdf/discovering-visualizing-and-sharing-knowledge-through-5du3gilk03.pdf

Discovering, visualizing, and sharing knowledge through personalized learning knowledge maps

This paper discusses some implications of knowledge creation processes in informal social networks for the development of technologies to support them. The principal point of departure are social theories of learning and the theories of organisational knowledge creation. The focus is on models for the exchange and sharing of implicit knowledge. A model of personalised learning knowledge maps is presented as one possible way of addressing the problem of capturing, visualising and sharing implicit knowledge of a community of users. In particular, we discuss how this model resolves one critical shortcoming of the existing socialisation and externalisation approaches: the creation of a semantic representation of a shared understanding of the community which reflects implicit knowledge and incorporates personal views of individual users. Finally, we outline the application to a real-world interdisciplinary Internet platform netzspannung.org.

/pdf/supporting-knowledge-creation-and-sharing-in-communities-1a2tv2xuog.pdf

Supporting Knowledge Creation and Sharing in Communities Based on Mapping Implicit Knowledge.

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