parand (26 Ergebnisse)

Paperback. Zustand: Good. International Edition. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience. International Edition.

Paperback. Zustand: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Zustand: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: New. pp. 304.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Brand New. 252 pages. 6.00x0.68x9.00 inches. In Stock.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Zustand: Brand New. 252 pages. 6.00x0.81x9.00 inches. In Stock.

Zustand: New. An anthology of contemporary women s short fiction in translation, featuring work by emerging women writers from across Afghanistan.KlappentextrnrnAn anthology of contemporary women s short fiction in translation, featuring work by emerging .

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Mandy: An Interstellar Romance displays natural and aberrant relationships among a set of international characters capable of the highest nobility and the lowest behavior.Set against the techno-spiritual bond between a scientist and his creation, the story considers the character of humanity that can demonstrate extreme malevolence, as well as ambivalent ethical nuance.The cherished wife of a rich Iranian gunrunner is murdered by competitors. He sends his two sons, William and Alex, away to be brought up by their nanny. William becomes a rich businessman, while Alex develops into a world-renowned scientist, whose computerized robot, Mandy, is sent to explore the universe. Alex has been developing Mandy since his youth, carrying on a close man-machine relationship throughout the novel.Growing up isolated from family, the brothers' devotion for each other is as close as possible. Then a real woman enters the picture. The beautiful Yassi is a wholly amoral temptress who seduces men and takes their money. She has her sights set on both brothers.(About the Author)Dr. Parand Alavi was born in Iran, grew up in Iraq, and attended medical school in France. She took up residence in northern Virginia 45 years ago, after passing a Board of Internal Medicine examination for practice in America. In 2002, Dr. Alavi began painting as a hobby. Her subjects most often dealt with Kurdish women dressed in their traditionally colorful garments. However, writing has come easier. Her first five Persian novels, The Empty Balloon, Mirage of Freedom, Broken Mast, Two Straw, and Dandelion are widely popular in Iran. Dandelions and Roses - A Contemporary Persian Romance is the English translation of Dandelion. A translation of the autobiographic Two Straw is planned for release soon.

Buch. Zustand: Neu. Neuware - Carefully curated and translated by Dr. Manijeh Rabiei, Persian Garden of Imagination breathes new life into these ancient texts that shed light on Iranian culture and mythology. The literary stories of Iran contain complex topics and symbols related to the region's classic, pre-Abrahamic faiths, including Zoroastrianism, Mithraism, and mysticism.This collection comprises thirty stories that began in the oral storytelling tradition from before the written word was created. For millennia, the folklore found in this book was used as a way for people to understand their fears, to express their culture's worldview, and to interpret the deep and fascinating history of their region.Filled with beautiful and evocative literary art, tales of deception, and stories of enduring love, Persian Garden of Imagination captures the essence of the Persian belief system and provides essential reading for anyone craving knowledge about this mesmerizing part of the world.

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

Zustand: New.

Paperback. Zustand: Brand New. 250 pages. 9.25x7.50x9.22 inches. In Stock.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 319 | Sprache: Englisch | Produktart: Bücher | This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions¿Chebyshev, Legendre, Gegenbauer, and Jacobi¿are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 320 | Sprache: Englisch | Produktart: Bücher | This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions¿Chebyshev, Legendre, Gegenbauer, and Jacobi¿are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.

Taschenbuch. Zustand: Neu. Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines | Theory, Algorithms and Applications | Jamal Amani Rad (u. a.) | Taschenbuch | xiv | Englisch | 2024 | Springer | EAN 9789811965555 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Neuware -This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions¿Chebyshev, Legendre, Gegenbauer, and Jacobi¿are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 320 pp. Englisch.

Buch. Zustand: Neu. Neuware -This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions¿Chebyshev, Legendre, Gegenbauer, and Jacobi¿are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 320 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions-Chebyshev, Legendre, Gegenbauer, and Jacobi-are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions-Chebyshev, Legendre, Gegenbauer, and Jacobi-are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.

Hardcover. Zustand: Brand New. 319 pages. 9.25x6.10x9.21 inches. In Stock.

Zustand: New.

Paperback. Zustand: Brand New. 250 pages. 9.25x7.50x9.22 inches. In Stock.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware - Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.