Search Results - (((((((want OR when) OR semantic) OR arts) OR cantor) OR anne) OR maarten) OR santis) algorithms.

Swift 2 design patterns : build robust and scalable iOS and Mac OS X game applications by Lange, Julien

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Semi-supervised learning

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Kernels for structured data by Gärtner, Thomas

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Computational Continuum Mechanics. by Shabana, Ahmed A.

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Integration of demand response into the electricity chain : challenges, opportunities and smart grid solutions

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Applied integer programming : modeling and solution by Chen, Der-San, 1940-

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Modern aerodynamic methods for direct and inverse applications by Chin, Wilson C.

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Image processing and jump regression analysis by Qiu, Peihua, 1965-

Table of Contents: “…Cover -- Contents -- Preface -- 1 Introduction -- 1.1 Images and image representation -- 1.2 Regression curves and sugaces with jumps -- 1.3 Edge detection, image restoration, and jump regression analysis -- 1.4 Statistical process control and some other related topics -- 1.5 Organization of the book -- Problems -- 2 Basic Statistical Concepts and Conventional Smoothing Techniques -- 2.1 Introduction -- 2.2 Some basic statistical concepts and terminologies -- 2.2.1 Populations, samples, and distributions -- 2.2.2 Point estimation of population parameters -- 2.2.3 Confidence intervals and hypothesis testing -- 2.2.4 Maximum likelihood estimation and least squares estimation -- 2.3 Nadaraya- Watson and other kernel smoothing techniques -- 2.3.1 Univariate kernel estimators -- 2.3.2 Some statistical properties of kernel estimators -- 2.3.3 Multivariate kernel estimators -- 2.4 Local polynomial kernel smoothing techniques -- 2.4.1 Univariate local polynomial kernel estimators -- 2.4.2 Some statistical properties -- 2.4.3 Multivariate local polynomial kernel estimators -- 2.4.4 Bandwidth selection -- 2.5 Spline smoothing procedures -- 2.5.1 Univariate smoothing spline estimation -- 2.5.2 Selection of the smoothing parameter -- 2.5.3 Multivariate smoothing spline estimation -- 2.5.4 Regression spline estimation -- 2.6 Wavelet transformation methods -- 2.6.1 Function estimation based on Fourier transformation -- 2.6.2 Univariate wavelet transformations -- 2.6.3 Bivariate wavelet transformations -- Problems -- 3 Estimation of Jump Regression Curves -- 3.1 Introduction -- 3.2 Jump detection when the number of jumps is known -- 3.2.1 Difference kernel estimation procedures -- 3.2.2 Jump detection based on local linear kernel smoothing -- 3.2.3 Estimation of jump regression functions based on semiparametric modeling -- 3.2.4 Estimation of jump regression functions by spline smoothing -- 3.2.5 Jump and cusp detection by wavelet transformations -- 3.3 Jump estimation when the number of jumps is unknown -- 3.3.1 Jump detection by comparing three local estimators -- 3.3.2 Estimation of the number of jumps by a sequence of hypothesis tests -- 3.3.3 Jump detection by DAKE -- 3.3.4 Jump detection by local polynomial regression -- 3.4 Jump-preserving curve estimation -- 3.4.1 Jump curve estimation by split linear smoothing -- 3.4.2 Jump-preserving curve fitting based on local piecewise-linear kernel estimation -- 3.4.3 Jump-preserving smoothers based on robust estimation -- 3.5 Some discussions -- Problems -- 4 Estimation of Jump Location Curves of Regression Surfaces -- 4.1 Introduction -- 4.2 Jump detection when the number of jump location curves is known -- 4.2.1 Jump detection by RDKE -- 4.2.2 Minimax edge detection -- 4.2.3 Jump estimation based on a contrast statistic -- 4.2.4 Algorithms for tracking the JLCs -- 4.2.5 Estimation of JLCs by wavelet transformations -- 4.3 Detection of arbitrary jumps by local smoothing -- 4.3.1 Treat JLCs as a pointset in the design space -- 4.3.2 Jump detection by local linear estimation -- 4.3.3 Two modijication procedures -- 4.4 Jump detection in two or more given directions -- 4.4.1 Jump detection in two given directions -- 4.4.2 Measuring the p.…”
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Bayesian Analysis with Python. by Osvaldo Martin

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Eat, cook, grow : mixing human-computer interactions with human-food interactions

Table of Contents: “…"You don't have to be a gardener to do urban agriculture": understanding opportunities for designing interactive technologies to support urban food production / William Odom -- Augmented agriculture, algorithms, aerospace, and alimentary architectures / Jordan Geiger -- The allure of provenance: tracing food through user-generated production information / Ann Light -- Beyond gardening: a new approach to HCI and urban agriculture / Tad Hirsch -- Hungry for data: metabolic interaction from farm to fork to phenotype / Marc Tuters and Denisa Kera -- Food futures: three provocations to challenge HCI interventions / Greg Hearn and David Lindsay Wright -- Bringing technology to the dining table / Charles Spence -- List of recipes.…”
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FRBR, before and after : a look at our bibliographic models by Coyle, Karen

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Automatic indexing and abstracting of document texts by Moens, Marie-Francine, 1957-

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DEEP LEARNING FOR HEALTHCARE DECISION MAKING.

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Machine learning for dummies by Mueller, John, 1958-, Massaron, Luca

Table of Contents: “…Discovering the fad uses of AI and machine learning -- Considering the true uses of AI and machine learning -- Being useful -- being mundane -- Considering the Relationship between AI and Machine Learning -- Considering AI and Machine Learning Specifications -- Defining the Divide between Art and Engineering -- Chapter 2 Learning in the Age of Big Data -- Defining Big Data -- Considering the Sources of Big Data -- Building a new data source -- Using existing data sources -- Locating test data sources -- Specifying the Role of Statistics in Machine Learning -- Understanding the Role of Algorithms.…”
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Fast finance : does the financial world allow social loafing? by Williams, Gervais

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Google's PageRank and Beyond - the Science of Search Engine Rankings. by Langville, Amy N., Meyer, Carl D.

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Optimisation in Economic Analysis. by Mills, Gordon

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Large-scale computing techniques for complex system simulations by Dubitzky, Werner

Table of Contents: “…Front Matter -- State-of-the-Art Technologies for Large-Scale Computing / Florian Feldhaus, Stefan Freitag, Chaker El Amrani -- The e-Infrastructure Ecosystem: Providing Local Support to Global Science / Erwin Laure, Aʻke Edlund -- Accelerated Many-Core GPU Computing for Physics and Astrophysics on Three Continents / Rainer Spurzem, Peter Berczik, Ingo Berentzen, Wei Ge, Xiaowei Wang, Hsi-yu Schive, Keigo Nitadori, Tsuyoshi Hamada, Još Fiestas -- An Overview of the SimWorld Agent-Based Grid Experimentation System / Matthias Scheutz, Jack J Harris -- Repast HPC: A Platform for Large-Scale Agent-Based Modeling / Nicholson Collier, Michael North -- Building and Running Collaborative Distributed Multiscale Applications / Katarzyna Rycerz, Marian Bubak -- Large-Scale Data-Intensive Computing / Mark Parsons -- A Topology-Aware Evolutionary Algorithm for Reverse-Engineering Gene Regulatory Networks / Martin Swain, Camille Coti, Johannes Mandel, Werner Dubitzky -- QosCosGrid e-Science Infrastructure for Large-Scale Complex System Simulations / Krzysztof Kurowski, Bartosz Bosak, Piotr Grabowski, Mariusz Mamonski, Tomasz Piontek, George Kampis, L̀szl̤ Gulỳs, Camille Coti, Thomas Herault, Franck Cappello -- Glossary -- Index -- Wiley Series on Parallel and Distributed Computing.…”
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Understanding large temporal networks and spatial networks : exploration, pattern searching, visualization and network evolution by Batagelj, Vladimir, 1948-

Table of Contents: “…Spanish Algorithms -- 3.8.4.A Sparse Network Algorithm -- 3.9. …”
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Android Sensor Programming By Example. by Nagpal, Varun

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