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

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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.

Meta-clustering is a popular approach to find multiple clusterings in the datasest, which takes a large number of base clusterings as input for further user navigation and refinement. However, the effectiveness of meta-clustering is highly dependent on the distribution of the base clusterings and open challenges exist with regard to its stability and noise tolerance. In this paper we propose a simple and effective filtering algorithm (FILTA) that can be flexibly used in conjunction with any meta-clustering method. Given a (raw) set of base clusterings, FILTA employs information theoretic criteria to remove those having poor quality or high redundancy. Then this filtered set of clusterings is highly suitable for further exploration, particularly the use of visualization for determining the dominant views in the dataset. We evaluate FILTA on both synthetic and real world datasets, and see how its use can enhance view discovery for complex scenarios.

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FILTA: better view discovery from collections of clusterings via filtering

RegHunter: Using Graph partitioning to discover regions of correlated spatio-temporal change in evolving graphsIntelligent Data Analysis.

CANDECOMP/PARAFAC Decomposition (CPD) is one of the most popular tensor decomposition methods that has been extensively studied and widely applied. In recent years, sparse tensors that contain a huge portion of zeros but a limited number of non-zeros have attracted increasing interest. Existing techniques are not directly applicable to sparse tensors, since they mainly target dense ones and usually have poor efficiency. Additionally, specific issues also arise for sparse tensors, depending on different data sources and applications: the role of zero entries can be different; incorporating constraints like non-negativity and sparseness might be necessary; the ability to learn on-the-fly is a must for dynamic scenarios that new data keeps arriving at high velocity. However, state-of-art algorithms only partially address the above issues. To fill this gap, we propose a general framework for finding the CPD of sparse tensors. Modeling the sparse tensor decomposition problem by a generalized weighted CPD formulation and solving it efficiently, our proposed method is also flexible to handle constraints and dynamic data streams. Through experiments on both synthetic and real-world datasets, for the static case, our method demonstrates significant improvements in terms of effectiveness, efficiency and scalability. Moreover, under the dynamic setting, our method speeds up current technology by hundreds to thousands times, without sacrificing decomposition quality.

SCED: A General Framework for Sparse Tensor Decomposition with Constraints and Elementwise Dynamic Learning

© 2018 ASCILITE 2018 - Conference Proceedings - 35th International Conference of Innovation, Practice and Research in the use of Educational Technologies in Tertiary Education: Open Oceans: Learning Without Borders A considerable amount of research on emotions and learning has been undertaken in recent years. Confusion has been noted as a particularly important emotion as it has the potential to trigger students’ engagement in learning tasks. However, unresolved confusion may turn into frustration, boredom and ultimately disengagement. The study reported in this paper investigated whether learning analytics could be used to successfully determine indicators or patterns of interactions that may be associated with confusion in a simulation-based learning environment. The findings of the study indicated that when taken individually, measures on specific learning tasks only hint at when students are struggling, but when taken together these indicators present a pattern of student interactions or a student profile that could be indicative of confusion.

Struggle town? Developing profiles of student confusion in simulation-based learning environments

A number of techniques have been developed to facilitate
powerful data retrieval on the Web and Semantic Web. Three categories
of Web query languages can be distinguished, according to the format
of the data they can retrieve: XML, RDF and Topic Maps. This article
introduces the spectrum of languages falling into these categories
and summarises their salient aspects. The languages are introduced using
common sample data and query types. Key aspects of the query
languages considered are stressed in a conclusion.

James Bailey

Papers

FILTA: better view discovery from collections of clusterings via filtering

RegHunter: Using Graph partitioning to discover regions of correlated spatio-temporal change in evolving graphsIntelligent Data Analysis.

SCED: A General Framework for Sparse Tensor Decomposition with Constraints and Elementwise Dynamic Learning

Struggle town? Developing profiles of student confusion in simulation-based learning environments

Web and Semantic Web Query Languages