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.

Sequential patterns are used to discover knowledge in a wide range of applications. However, in many scenarios pattern quality can be low, due to short lengths or low supports. Furthermore, for dense datasets such as proteins, most of the sequential pattern mining algorithms return a tremendously large number of patterns, which are difficult to process and analyze. However, by relaxing the definition of frequency and allowing some mismatches, it is possible to discover higher quality patterns. We call these patterns Frequent Approximate Substrings or FAS-patterns and we introduce an algorithm called FAS-Miner, to handle the mining task efficiently. The experiments on real-world protein and DNA datasets show that FAS-Miner can discover patterns of much longer lengths and higher supports than standard sequential mining approaches.

/pdf/an-efficient-technique-for-mining-approximately-frequent-3qe56fdtzu.pdf

An Efficient Technique for Mining Approximately Frequent Substring Patterns

The iVAT (asiVAT) algorithms reorder symmetric (asymmetric) dissimilarity data so that an image of the data may reveal cluster substructure. Images formed from incomplete data don't offer a very rich interpretation of cluster structure. In this paper, we examine four methods for completing the input data with imputed values before imaging. We choose a best method using contaminated versions of the complete Iris data, for which the desired results are known. Then, we analyze two real world data sets from social networks that are incomplete using the best imputation method chosen in the juried trials with Iris: (i) Sampson's monastery data, an incomplete, asymmetric relation matrix; and (ii) the karate club data, comprising a symmetric similarity matrix that is about 86 percent incomplete.

Visual Assessment of Clustering Tendency for Incomplete Data

This paper presents a wavelet-based texture analysis method for classification of melanoma. The method applies tree-structured wavelet transform on different color channels of red, green, blue and luminance of dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. Feature extraction and a two-stage feature selection method, based on entropy and correlation, were applied to a train set of 103 images. The resultant feature subsets were then fed into four different classifiers: support vector machine, random forest, logistic model tree and hidden naive bayes to classify melanoma in a test set of 102 images, which resulted in an accuracy of 88.24% and ROC area of 0.918. Comparative study carried out in this paper shows that the proposed feature extraction method outperforms three other wavelet-based approaches.

/pdf/classification-of-melanoma-lesions-using-wavelet-based-2uwptsmo30.pdf

Classification of Melanoma Lesions Using Wavelet-Based Texture Analysis

BDI (Belief, Desire, Intention) is a mature and commonly adopted architecture for intelligent agents. However, the current computational model adopted by BDI has a number of problems with concurrency control, recoverability and predictability. This has hindered the construction of agents having robust and predictable behaviour. To this end, we propose to integrate distributed transactions, a well-established technology in distributed systems, into the computational model of multi-agent systems based on the BDI architecture. Differently from common approaches, where so-called ACID (Atomic, Consistent, Isolated, Durable) transactions are used simply to operate on external resources such as databases, in our model transactions are the foundation of the operational semantics of intentions and of collaborative tasks within team of agents. They provide a predictable, well understood behaviour in case of partial or total failure of intentions to achieve their goals or even crashes of agents. Furthermore, distributed transactions provide a simple and clear extension of the BDI semantics from the single-agent case to teams of agents. We discuss the development of an agent system having a computational model with well-defined correctness criteria. Instead of hardwiring robustness and fault-tolerant behaviour into agent plans, well defined notions of correctness exist at the semantic level. Verification can then be undertaken at the desired level of abstraction. Two BDI interpreter prototypes have been developed to demonstrate the feasibility of our approach. The first, TOMAS, is a Java environment that execute intentions as nested transactions. The second is a re-implementation of TOMAS within a J2EE application server, which can be used to develop session beans (i.e., business logic); it demonstrates how the model we propose nicely fits into a state-of-the-art environment for mission critical systems in domains such as e-business and Web services.

A reliable computational model for BDI Agents

Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage. By using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer. We show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes.

/pdf/a-voting-approach-to-identify-a-small-number-of-highly-4m3yc0lf4g.pdf

James Bailey

Papers

An Efficient Technique for Mining Approximately Frequent Substring Patterns

Visual Assessment of Clustering Tendency for Incomplete Data

Classification of Melanoma Lesions Using Wavelet-Based Texture Analysis

A reliable computational model for BDI Agents

A voting approach to identify a small number of highly predictive genes using multiple classifiers