luo xin (33 Ergebnisse)

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

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

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

paperback. Zustand: Very Good.

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

Zustand: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned. A few small stains.

Paperback. Zustand: Brand New. 100 pages. 9.25x6.10x0.32 inches. In Stock.

Paperback. Zustand: Brand New. 88 pages. 9.25x6.10x0.19 inches. In Stock.

Paperback. Zustand: Brand New. 136 pages. 9.25x6.10x0.30 inches. In Stock.

Zustand: New. 2012. Bilingual. Hardcover. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Neuware -Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question.This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications.The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 100 pp. Englisch.

Hardcover. Zustand: Brand New. bilingual edition. 707 pages. 9.25x6.25x1.75 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware -This book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; RNN for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration ¿ Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm ¿ which can effectively enhance robot positioning accuracy.In addition, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies without calibration data. Thus, this book provides a publicly available dataset to assist researchers from other fields in conductingcalibration experiments and validating their ideas. The book also discusses six regularization schemes based on its robot error models, i.e., L1, L2, dropout, elastic, log, and swish. Robots¿ positioning accuracy is significantly improved after calibration. Using the control and calibration methods developed here, readers will be ready to conduct their own research and experiments.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 140 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; RNN for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration - Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm - which can effectively enhance robot positioning accuracy.In addition, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies without calibration data. Thus, this book provides a publicly available dataset to assist researchers from other fields in conductingcalibration experiments and validating their ideas. The book also discusses six regularization schemes based on its robot error models, i.e., L1, L2, dropout, elastic, log, and swish. Robots' positioning accuracy is significantly improved after calibration. Using the control and calibration methods developed here, readers will be ready to conduct their own research and experiments.

Zustand: Bon. Merci, votre achat aide à financer des programmes de lutte contre l'illettrisme.

Zustand: Assez bon. Merci, votre achat aide à financer des programmes de lutte contre l'illettrisme.

Zustand: New. &Uumlber den AutorrnrnZhu Yuqi is Chief Editor of LIterature and History of the Western Regions and Professor at Peking University.Andrew Eisenberg is author of Kingship in Early Medieval China and Professor at Northeast.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question.This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications.The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes¿ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge.In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.

Buch. Zustand: Neu. Neuware - Zhu Yuqi is Chief Editor of LIterature and History of the Western Regions and Professor at Peking University.

Hardcover. Zustand: Brand New. 137 pages. 9.25x6.10x0.38 inches. In Stock.

Buch. Zustand: Neu. Neuware -This book presents a collection of papers presented at the 3rd World Congress on Integrated Computational Materials Engineering (ICME), a specialty conference organized by The Minerals, Metals & Materials Society (TMS). This meeting convened ICME stakeholders to examine topics relevant to the global advancement of ICME as an engineering discipline.The papers presented in these proceedings are divided into six sections: (1) ICME Applications; (2) ICME Building Blocks; (3) ICME Success Stories and Applications (4) Integration of ICME Building Blocks: Multi-scale Modeling; (5) Modeling, Data and Infrastructure Tools, and (6) Process Optimization. .These papers are intended to further the global implementation of ICME, broaden the variety of applications to which ICME is applied, and ultimately help industry design and produce new materials more efficiently and effectively.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 392 pp. Englisch.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a collection of papers presented at the 3rd World Congress on Integrated Computational Materials Engineering (ICME), a specialty conference organized by The Minerals, Metals & Materials Society (TMS). This meeting convened ICME stakeholders to examine topics relevant to the global advancement of ICME as an engineering discipline.The papers presented in these proceedings are divided into six sections: (1) ICME Applications; (2) ICME Building Blocks; (3) ICME Success Stories and Applications (4) Integration of ICME Building Blocks: Multi-scale Modeling; (5) Modeling, Data and Infrastructure Tools, and (6) Process Optimization. .These papers are intended to further the global implementation of ICME, broaden the variety of applications to which ICME is applied, and ultimately help industry design and produce new materials more efficiently and effectively.

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

Hardcover. Zustand: Brand New. 391 pages. 9.25x6.10x0.98 inches. In Stock.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 400 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Zustand: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned. A few small stains.

Soft cover. Zustand: Fine. No Jacket. 1 v. (unpaged) : ill. ; 27 cm. ; Sale code: 6027 QICHANG/ Place of sale: New York ; OCLC: 477122841 ; color photographic stiff paper wrappers ; prices realized laid in ; features works by Zhao Mengfu, Jie Jisi, Ni Zan, hu Tinghui, Zheng Yuanyou, Dai Jin, Shen Zhou, Wu Wei, Tang Yin, Zhu Yunming, Wen Zhengming, Chen Shun, Qiu Ying, Qian Gu, Yu Yuanlun, Fan Dongming, Dong Qichang, Chen Jiru, Qian Gong, Huang Biao, Guan Si, Zhang Ruitu, Wang Duo, Lan Ying, Huang Xiangjian, Wang Jian, Xie Bin, Gao Cen, Li Yin, Mei Qing, Da Chongguang, Lai Jing, Zha Shibiao, Sheng Maojun, Zhang Qi, Wu Hong, Ma Quan, Qin Ji, Ye Xin, Fu Shan, Huang Daoqian, Jiang Shijie, Luo Mu, Fa Rouzhen, Wang Hui, Ma Yuanyu, Li Shan, Jiao Bingzhen, Wang Gang, ao Qipei, Jiang Tingxi, Hua Yan, Jin Nong, Zhang Ruoai, Zheng Xie, Huang Shen, Zhang Ruocheng, Dong Bangda, Wang Chen, Qian Feng, Yuan Ying, Fang Xun, Luo Ping, Pan Gongshou, Jiang Wenzai, Sun Shigao, Xi Gang, Tie Bao, Yuan Pei, Zhai Jichang, Weng Luo, Zhu Haonian, Shen Rong, Tang Yifen, Mao Zhou, Qu Yingshao, Su Renshan, Dai Xi, He Shaoji, Su Liupeng, Peng Yulin, Ren Yi, Wu Changshuo, Gu Linshi, Zeng Xi, Qi Baishi, Pu Ru, Pu Jin, Fu Baoshi, Feng Zhikai, Ding Yanyon, Zhang DaiqianLin FengmianWang Kangle, He Haixia, Xu Zhe, Li Xiongcai, Wu Guanzhong, Zhang Shoucheng, Cheng Shifa ; FINE. Book.

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.