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.

The local intrinsic dimensionality (LID) model enables assessment of the complexity of the local neighbourhood around a specific query object of interest. In this paper, we study variations in the LID of a query, with respect to different subspaces and local neighbourhoods. We illustrate the surprising phenomenon of how the LID of a query can substantially decrease as further features are included in a dataset. We identify the role of two key feature properties in influencing the LID for feature combinations: correlation and dominance. Our investigation provides new insights into the impact of different feature combinations on local regions of the data.

Characteristics of Local Intrinsic Dimensionality (LID) in Subspaces: Local Neighbourhood Analysis

In time series data, consecutively repeated patterns occur in many applications, including activity recognition from wearable sensors. Repeating patterns may vary over time and present in various shapes and sizes, which makes their detection a challenging problem. We develop a novel technique, RP-Mask, that can detect and localise segments of consecutively repeated patterns, without prior knowledge about the shape and length of the repeats. Our technique represents time series using recurrence plots (RP), a method for visualising repetition in time series. We identify two key features of recurrence plots-checkerboard patterns and vertical/horizontal lines marking the start and end of checkerboard patterns. We use object recognition on RP images to detect and localise the checkerboard patterns, which are mapped to the segments of consecutively repeating patterns on the underlying time series. Since the collection and labeling of a real world dataset that exhibits all possible variations of a repetition is prohibitive, we demonstrate that our model is able to effectively learn from synthetically curated data and perform equally effective on a real world dataset, while it is noise tolerant. We compare our method to a number of state-of-the-art techniques and show that our method outperforms the state of the art both when trained using real activity recognition and synthetic data.

A Transferable Technique for Detecting and Localising Segments of Repeating Patterns in Time series

This paper details a novel response to the problem of how to provide technology-enhanced feedback to students undertaking projects as part of a Masters in Information Systems. It contributes an approach to designing a promising new user-centered tool to motivate active learning. It reports on a case study
where staff join a participatory design process to develop an LMS integrated tool. This tool incorporates a traffic-light style presentation which is proposed to motivate completion of milestones in project based
assessment tasks. The unique tool provides an opportunity for feedback and to support students’ self regulation of their study behavior, especially those students who are not meeting milestones, and encourages students to engage with staff to remedy academic problems before they become severe. The tool is also proposed as a means to reduce the administrative and assessment burden on staff while supporting them in fulfilling their responsibilities to the students they supervise.

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1035&context=icis2017

Using a Traffic Light System to Provide Feedback to IS Masters Students

© Springer Nature Switzerland AG 2019. Big data analytics typically requires large amounts of resources to process ever-increasing data volumes. This can be time consuming and result in considerable expenses. Analytics-as-a-Service (AaaS) platforms provide a way to tackle expensive resource costs and lengthy data processing times by leveraging automatic resource management with a pay-per-use service delivery model. This paper explores optimization of resource management algorithms for AaaS platforms to automatically and elastically provision cloud resources to execute queries with Service Level Agreement (SLA) guarantees. We present admission control and cloud resource scheduling algorithms that serve multiple objectives including profit maximization for AaaS platform providers and query time minimization for users. Moreover, to enable queries that require timely responses and/or have constrained budgets, we apply data sampling-based admission control and resource scheduling where accuracy can be traded-off for reduced costs and quicker responses when necessary. We conduct extensive experimental evaluations for the algorithm performances compared to state-of-the-art algorithms. Experiment results show that our proposed algorithms perform significantly better in increasing query admission rates, consuming less resources and hence reducing costs, and ultimately provide a more flexible resource management solution for fast, cost-effective, and reliable big data processing.

Profit maximization and time minimization admission control and resource scheduling for cloud-based big data analytics-as-a-service platforms

A key application of wearable sensors is remote patient monitoring, which facilitates clinicians to observe patients non-invasively, by examining the time series of sensor readings. For analysis of such time series, a recently proposed technique is Matrix Profile (MP). While being effective for certain time series mining tasks, MP depends on a key input parameter, the length of subsequences for which to search. We demonstrate that MP’s dependency on this input parameter impacts its effectiveness for finding patterns of interest. We focus on finding consecutive repeating patterns (CRPs), which represent human activities and exercises whilst tracked using wearable sensors. We demonstrate that MP cannot detect CRPs effectively and extend it by adding a locality preserving index. Our method automates the use of MP, and reduces the need for data labeling by experts. We demonstrate our algorithm’s effectiveness in detecting regions of CRPs through a number of real and synthetic datasets.

James Bailey

Papers

Characteristics of Local Intrinsic Dimensionality (LID) in Subspaces: Local Neighbourhood Analysis

A Transferable Technique for Detecting and Localising Segments of Repeating Patterns in Time series

Using a Traffic Light System to Provide Feedback to IS Masters Students

Profit maximization and time minimization admission control and resource scheduling for cloud-based big data analytics-as-a-service platforms

An Automated Matrix Profile for Mining Consecutive Repeats in Time Series