skillicorn david (205 risultati)

Softcover. Condizione: Très bon. Ancien livre de bibliothèque. Salissures sur la tranche. Couverture différente. Edition 1994. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Stains on the edge. Different cover. Edition 1994. Ammareal gives back up to 15% of this item's net price to charity organizations.

Hardcover. Condizione: Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.15.

Condizione: NEW.

Condizione: New. 2020. 1st Edition. Hardcover. . . . . .

Paperback. Condizione: Good. ***GOOD CONDITION***CLEAN PAGES***EX-LIBRARY stamps stickers/marked out name, moderate edge wear, stamp on top/side/bottom,sticker remained on side of book & on inside of title page. We specialize in non-fiction books. Follow us on Facebook and Instagram.

Hardcover. Condizione: Fine.

Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 332 | Sprache: Englisch | Produktart: Bücher.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. Such spaces are not easy to work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear information as we might expect. There are three main areas where complex high dimensionality and large datasets arise naturally: data collected by online retailers, preference sites, and social media sites, and customer relationship databases, where there are large but sparse records available for each individual; data derived from text and speech, where the attributes are words and so the corresponding datasets are wide, and sparse; and data collected for security, defense, law enforcement, and intelligence purposes, where the datasets arelarge and wide. Such datasets are usually understood either by finding the set of clusters they contain or by looking for the outliers, but these strategies conceal subtleties that are often ignored. In this book the author suggests new ways of thinking about high-dimensional spaces using two models: a skeleton that relates the clusters to one another; and boundaries in the empty space between clusters that provide new perspectives on outliers and on outlying regions. The book will be of value to practitioners, graduate students and researchers.

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Condizione: New. xv, 257 pages, illustrations black and white.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: new. Paperback. The major reason for the lack of use of parallel computing is the mismatch between the complexity and variety of parallel hardware, and the software development tools to program it. The cost of developing software needs to be amortised over decades, but the platforms on which it executes change every few years, requiring complete rewrites. The evident cost-effectiveness of parallel computation has not been realized because of this mismatch. This book presents an integrated approach to parallel software development by addressing both software and performance issues together. It presents a methodology for software construction that produces architecture - independent and intellectually abstract software. The software can execute efficiently on a range of existing and potential hardware configurations. The approach is based on the construction of categorical data types, a generalization of abstract data types, and of objects. Categorical data types abstract both from the representation of a data type, and also from the detailed control flow necessary to perform operations on it. They thus impose a strong separation between the semantics, on which programs can depend, and the implementation, which is therefore free to hide the parallel machine properties that are used. Using parallel machines is difficult because of their inherent complexity and because their architecture changes frequently. This book presents an integrated approach to developing software for parallel machines that addresses software issues and performance issues together. The author describes a methodology for software construction that is architecture-independent and intellectually abstract. The software can execute efficiently on a range of existing and potential hardware configurations. The approach is based on the construction of categorical data types, a generalization of abstract data types, and of objects. This work will be an outstanding reference for researchers in computing. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Community detection in social networks is an important but challenging problem. This book develops a new technique for finding communities that uses both structural similarity and attribute similarity simultaneously, weighting them in a principled way. The results outperform existing techniques across a wide range of measures, and so advance the state of the art in community detection. Many existing community detection techniques base similarity on either the structural connections among social-network users, or on the overlap among the attributes of each user. Either way loses useful information. There have been some attempts to use both structure and attribute similarity but success has been limited. We first build a large real-world dataset by crawling Instagram, producing a large set of user profiles. We then compute the similarity between pairs of users based on four qualitatively different profile properties: similarity of language used in posts, similarity of hashtags used (which requires extraction of content from them), similarity of images displayed (which requires extraction of what each image is 'about'), and the explicit connections when one user follows another. These single modality similarities are converted into graphs. These graphs have a common node set (the users) but different sets a weighted edges. These graphs are then connected into a single larger graph by connecting the multiple nodes representing the same user by a clique, with edge weights derived from a lazy random walk view of the single graphs. This larger graph can then be embedded in a geometry using spectral techniques. In the embedding, distance corresponds to dissimilarity so geometric clustering techniques can be used to find communities. The resulting communities are evaluated using the entire range of current techniques, outperforming all of them. Topic modelling is also applied to clusters to show that they genuinely represent users with similar interests. This can form the basis for applications such as online marketing, or key influence selection.

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