關於這本電子書
The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.
The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of indi...
探索更多
為這本電子書評分
請分享你的寶貴意見。
閱讀資訊
智能手機和平板電腦
手提電腦和電腦
你可以使用電腦的網絡瀏覽器聆聽在 Google Play 上購買的有聲書。
電子書閱讀器及其他裝置
如要在 Kobo 等電子墨水裝置上閱覽書籍，你需要下載檔案並傳輸到你的裝置。請按照說明中心的詳細指示，將檔案傳輸到支援的電子書閱讀器。
