Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

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

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.

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A Survey on Transfer Learning

Clustering depends critically on density and distance (similarity), but these concepts become increasingly more difficult to define as dimensionality increases. In this paper we offer definitions of density and similarity that work well for high dimensional data (actually, for data of any dimensionality). In particular, we use a similarity measure that is based on the number of neighbors that two points share, and define the density of a point as the sum of the similarities of a point’s nearest neighbors. We then present a new clustering algorithm that is based on these ideas. This algorithm eliminates noise (low density points) and builds clusters by associating non-noise points with representative or core points (high density points). This approach handles many problems that traditionally plague clustering algorithms, e.g., finding clusters in the presence of noise and outliers and finding clusters in data that has clusters of different shapes, sizes, and density. We have used our clustering algorithm on a variety of high and low dimensional data sets with good results, but in this paper, we present only a couple of examples involving high dimensional data sets: word clustering and time series derived from NASA Earth science data.

A New Shared Nearest Neighbor Clustering Algorithm and its Applications

Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. These discovered clusters are used to explain the characteristics of the data distribution. In this paper we propose a new methodology for clustering related items using association rules, and clustering related transactions using clusters of items. Our approach is linearly scalable with respect to the number of transactions. The frequent item-sets used to derive association rules are also used to group items into a hypergraph edge, and a hypergraph partitioning algorithm is used to nd the clusters. Our experiments indicate that clustering using association rule hypergraphs holds great promise in several application domains. Our experiments with stock-market data and congressional voting data show that this clustering scheme is able to successfully group items that belong to the same group. Clustering of items can also be used to cluster the transactions containing these items. Our experiments with congressional voting data show that this method is quite e ective in nding clusters of transactions that correspond to either democrat or republican voting patterns. Compared to the existing clustering algorithmAutoclass, our algorithm produced comparable quality clusters in the congressional voting data. This work was supported by NSF grant ASC-9634719, Army Research O ce contract DA/DAAH04-95-1-0538, Cray Research Inc. Fellowship, and IBM partnership award, the content of which does not necessarily re ect the policy of the government, and no ofcial endorsement should be inferred. Access to computing facilities was provided by AHPCRC, Minnesota Supercomputer Institute, Cray Research Inc., and NSF grant CDA-9414015. See http://www.cs.umn.edu/ han for an extended version of this paper and other related papers.

Clustering Based on Association Rule Hypergraphs

We propose an agent for exploring and categorizing documents on the World Wide Web based on a user pro le. The heart of the agent is an automatic categorization of a set of documents, combined with a process for generating new queries used to search for new related documents and ltering the resulting documents to extract the set of documents most closely related to the starting set. The document categories are not given a-priori. The resulting document set could also be used to update the initial set of documents. We present the overall architecture and describe two novel algorithms which provide signi cant improvement over traditional clustering algorithms and form the basis for the query generation and search component of the agent.

WebACE: a Web agent for document categorization and exploration

Analyzing scalability of parallel algorithms and architectures

The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes, and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this article, we provide a structured and comprehensive overview of data mining techniques for modeling EHRs. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.

https://dl.acm.org/doi/pdf/10.1145/3127881

Vipin Kumar

Papers

A New Shared Nearest Neighbor Clustering Algorithm and its Applications

Clustering Based on Association Rule Hypergraphs

WebACE: a Web agent for document categorization and exploration

Analyzing scalability of parallel algorithms and architectures

Mining Electronic Health Records (EHRs): A Survey