Li hang (63 Ergebnisse)

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

Zustand: Very Good. Very Good condition. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.

Zustand: Very Good. This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .…

paperback. Zustand: Very Good. Most items will be dispatched the same or the next working day. A copy that has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.

paperback. Zustand: Very Good. Most items will be dispatched the same or the next working day. A copy that has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Paperback. Zustand: Used; Very Good. Dispatched, from the UK, within 48 hours of ordering. Though second-hand, the book is still in very good shape. Minimal signs of usage may include very minor creasing on the cover or on the spine.

Softcover. Zustand: Bon. Ancien livre de bibliothèque avec équipements. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Ammareal gives back up to 15% of this item's net price to charity organizations.…

Softcover. Zustand: Très bon. Edition 2007. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Edition 2007. Ammareal gives back up to 15% of this item's net price to charity organizations.

Softcover. Zustand: Très bon. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Ammareal gives back up to 15% of this item's net price to charity organizations.

Softcover. Zustand: Très bon. Ancien livre de bibliothèque avec équipements. 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. Ammareal gives back up to 15% of this item's net price to charity organizations.…

paperback. Zustand: Very Good. Most items will be dispatched the same or the next working day. A copy that has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

2007. 16 x 24 cm. XXV, 1161 S. XXV, 1161 p. softcover Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. (Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence). Sprache: Englisch.…

Zustand: New. In English.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. pp. 712 Illus.

Zustand: New. In French.

Hardcover. Zustand: Brand New. illustrated edition. 312 pages. 11.00x7.75x1.00 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work. …

Taschenbuch. Zustand: Neu. Neuware - Make your own anime with this unique introductory guide to Japanese animation. You'll learn every stage of the animation process from scripting and storyboarding to preparing and distributing your film. Everything is clearly explained with step-by-step tutorials and packed with color screengrabs, stills and artwork illustrating every technique and process, including: \* Hand-painting characters and backgrounds on to separate cel layers\* Working with 3D graphics\* Using digital pen-and-tone techniquesApply the core style elements and visual language of anime to your own work and learn to:\* Simplify characters without losing their impact\* Create exaggerated facial expressions\* Use shadows and shading for dramatic effects\* Add lip syncing and speed lines to convey movement.…

Kartoniert / Broschiert. Zustand: New. Chi Hang Li, Chris Patmore, Hayden Scott BaronMake your own anime with this unique introductory guide to Japanese animation. You ll learn every stage of the animation process from scripting and storyboarding to preparing and distributing your film. E.

Zustand: New.

Taschenbuch. Zustand: Neu. Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition | Hang Li | Taschenbuch | Synthesis Lectures on Human Language Technologies | xiii | Englisch | 2014 | Springer | EAN 9783031010279 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Machine Learning Methods | Hang Li | Taschenbuch | xv | Englisch | 2024 | Springer | EAN 9789819939190 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: New. In English.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 547 | Sprache: Englisch | Produktart: Bücher | This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.…