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Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms : A Practical Approach Using Python
Describes the deep learning models and ensemble approaches applied to decision-making problems. The authors have addressed the concepts of deep learning, convolutional neural networks, recurrent neural networks, and ensemble learning in a practical sense providing complete code and implementation for several real-world examples. The authors of this book teach the concepts of machine learning for undergraduate and graduate-level classes and have worked with Fortune 500 clients to formulate data analytics strategies and operationalise these strategies.
Advanced Data Warehouse Design : From Conventional to Spatial and Temporal Applications
This book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources. Providing a clear and a concise presentation of the major concepts and results of data warehouse design, it can also be used as the basis of a graduate or advanced undergraduate course.
Advanced Data Mining Techniques
This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining. Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.
Advanced data mining and applications ; Vol. 4093 : 2nd International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006, Proceedings
This book constitutes the refereed proceedings of the Second International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China in August 2006. The 41 revised full papers and 74 revised short papers presented together with 4 invited papers were carefully reviewed and selected from 515 submissions. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.
Advanced data mining and applications ; Vol. 3584 ; 1st International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005, Proceedings
This book constitutes the refereed proceedings of the First International Conference on Advanced Data Mining and Applications, ADMA 2005, held in Wuhan, China in July 2005. The 25 revised full papers and 75 revised short papers presented were carefully peer-reviewed and The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.
Advanced Data Mining and Applications ; 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008. Proceedings
This book constitutes the refereed proceedings of the 4th International Conference on Advanced Data Mining and Applications, ADMA 2008, held in Chengdu, China, in October 2008.
Advanced Data Mining and Applications ; 3rd International Conference, ADMA 2007, Harbin, China, August 6-8, 2007 Proceedings
The Third International Conference on Advanced Data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by the first two ADMA conferences in Wuhan in 2005 and Xi’an in 2006. One major goal of ADMA is to create a respectable identity in the data mining research com- nity. This feat has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. The impact of a conference is measured by the citations the conference papers receive. Some have used this measure to rank conferences.
Advanced Algorithms and Data Structures
introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution. What's inside Build on basic data structures you already know Profile your algorithms to speed up application Store and query strings efficiently Distribute clustering algorithms with MapReduce Solve logistics problems using graphs and optimization algorithms
Advanced Air and Noise Pollution Control ; Vol.2
Handbook of Environmental Engineering, Volume 2, leading pollution control educators and practicing professionals describe how various combinations of different cutting-edge process systems can be arranged to solve air, noise, and thermal pollution problems. Each chapter discusses in detail a variety of process combinations, along with technical and economic evaluations, and presents explanations of the principles behind the designs, as well as numerous variant designs useful to practicing engineers. The emphasis throughout is on developing the necessary engineering solutions from fundamental principles of chemistry, physics, and mathematics. The authors also include extensive references, cost data, design methods, guidance on the installation and operation of various air pollution control process equipment and systems, and Best Available Technologies (BAT) for air, thermal, and noise pollution control. A companion volume, Air Pollution Control Engineering: Handbook of Environmental Engineering, Volume 1 critically surveys the principles and practices involved in basic air pollution control processes.
Advance Concepts of Image Processing and Pattern Recognition : Effective Solution for Global Challenges
Explains the important concepts and principles of image processing to implement the algorithms and techniques to discover new problems and applications. It contains numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. It presents essential background theory, shape methods, texture about new methods, and techniques for image processing and pattern recognition. It maintains a good balance between a mathematical background and practical implementation. This book also contains the comparison table and images that are used to show the results of enhanced techniques. This book consists of novel concepts and hybrid methods for providing effective solutions for society. It also includes a detailed explanation of algorithms in various programming languages like MATLAB, Python, etc.
Adobe® Acrobat® and PDF for Architecture, Engineering, and Construction
Adobe® Acrobat® and PDF for Architecture, Engineering, and Construction is designed to appeal to the engineering mind. The book is a practical guide focusing on the applications of PDF in the solution of "engineering" problems which may arise in a number of disciplines from architecture to construction. Using real-world examples, the authors follow a project from design through build and long-term maintenance. As the sample project evolves, suitable Acrobat® tools and techniques are identified and brought into play at each stage, showing readers how to personalize the context and processes to meet their own project development and management needs.
Ad-Hoc, mobile, and wireless networks ; Vol.3738 ; 4th International conference, ADHOC-NOW 2005, Cancun, Mexico, October 6-8, 2005, Proceedings
This book constitutes the refereed proceedings of the 4th International Conference on Ad-Hoc Networks and Wireless, ADHOiNOW 2005, The papers discuss architectures, protocols, and algorithms for: access control, scheduling, ad hoc and sensor networks analytic methods and modelling for performance evaluation, characterization, optimization, auto-configuration, incentives and pricing, location awareness, discovery, dependence, and management, mesh networks, new applications, power management, power control, and energy-efficiency, quality-of-service, resource allocation, multimedia, routing (unicast, multicast, etc.), security and privacy, service discovery, systems and testbeds, wireless internet, and data management.
Adaptive-robust control with limited knowledge on systems dynamics : An artificial input delay approach and beyond
investigates the role of artificial input delay in approximating unknown system dynamics, referred to as time-delayed control (TDC), and provides novel solutions to current design issues in TDC. Its central focus is on designing adaptive-switching gain-based robust control (ARC) for a class of Euler–Lagrange (EL) systems with minimal or no knowledge of the system dynamics parameters. The newly proposed TDC-based ARC tackles the commonly observed over- and under-estimation issues in switching gain. The consideration of EL systems lends a practical perspective on the proposed methods, and each chapter is supplemented by relevant experimental data
Adaptive Techniques for Mixed Signal System on Chip
Adaptive Techniques for Mixed Signal Sytem on Chip discusses the concept of adaptation in the context of analog and mixed signal design along with different adaptive architectures used to control any system parameter. The first part of the book gives an overview of the different elements that are normally used in adaptive designs including tunable elements as well as voltage, current, and time references with an emphasis on the circuit design of specific blocks such as voltage-controlled transconductors, offset comparators, and a novel technique for accurate implementation of on chip resistors. While the first part of the book addresses adaptive techniques at the circuit and block levels, the second part discusses adaptive equalization architectures employed to minimize the impact of ISI (Intersymbol Interference) on the quality of received data in high-speed wire line transceivers. It presents the implementation of a 125Mbps transceiver operating over a variable length of Category 5 (CAT-5) Ethernet cable as an example of adaptive equalizers.
Adaptive Techniques for Dynamic Processor Optimization : Theory and Practice
This book discusses the different approaches and responses to adaptive techniques used for processor power, frequency and functionality optimization. Adaptive Techniques for Dynamic Processor Optimization: Theory and Practice includes chapter contributions that explore promising approaches and present the supporting data.
Adaptive Multimedial Retrieval : Retrieval, User, and Semantics ; 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers
This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Adaptive Multimedia Retrieval, AMR 2007, held in Paris, France, in July 2007.
Adaptive Multimedia Retrieval : User, Context, and Feedback ; Third International Workshop, AMR 2005, Glasgow, UK, July 28-29, 2005, Revised Selected Papers
This book is an extended collection of revised contributions that were initially submitted to the International Workshop on Adaptive Multimedia Retrieval (AMR 2005). This workshop was organized during July 28-29, 2005, at the U- versity of Glasgow, UK, as part of an information retrieval research festival and in co-location with the 19th International Joint Conference on Arti?cial Int- ligence (IJCAI 2005). AMR 2005 was the third and so far the biggest event of the series of workshops that started in 2003 with a workshop during the 26th German Conference on Arti?cial Intelligence (KI 2003) and continued in 2004 as part of the 16th European Conference on Arti?cial Intelligence (ECAI 2004).
Adaptive Multimedia Retrieval : User, Context, and Feedback ; 4th International Workshop, AMR 2006, Geneva, Switzerland, July, 27-28, 2006, Revised Selected Papers
This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Adaptive Multimedia Retrieval, AMR 2006, held in Geneva, Switzerland in July 2006. this book provides a good and conclusive overview of the current research in the area of adaptive information retrieval.
Adaptive Machine Learning Algorithms with Python : Solve Data Analytics and Machine Learing Problems on Edge Devices
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. You will: Apply adaptive algorithms to practical applications and examples / Understand the relevant data representation features and computational models for time-varying multi-dimensional data / Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data / Speed up your algorithms and put them to use on real-world stationary and non-stationary data / Master the applications of adaptive algorithms on critical edge device computation applications
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
