|
||||||||||||||||||
|
||||||||||||||||||
|
||||||||||||||||||
よく一緒に購入されている商品
この商品を買った人はこんな商品も買っています
Xiaojin Zhu
|
Bernhard Sch¶lkopf
|
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) Carl Edward Rasmussen |
John Shawe-Taylor
|
Pattern Recognition and Machine Learning (Information Science and Statistics) Christopher M. Bishop |
商品の説明
内容説明
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.
Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
Adaptive Computation and Machine Learning series
Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
Adaptive Computation and Machine Learning series
登録情報
カタログ情報を更新するまたはイメージに対するお問い合わせ
|
この商品を見た後に買っているのは？
 |
58%のカスタマーが、このページの商品を購入しています。 Semi-Supervised Learning (Adaptive Computation and Machine Learning)￥ 5,297 |
 |
24%のカスタマーが Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)を購入しています￥ 3,704 |
 |
14%のカスタマーが Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)を購入しています￥ 3,334 |
 |
2%のカスタマーが Pattern Recognition and Machine Learning (Information Science and Statistics)を購入しています ￥ 10,864 |
この商品にタグをつける(詳細)タグは、商品との関連性が非常に強いキーワードまたはラベルのようなものです。
タグにより、すべてのお客様がお気に入りの商品の整理と確認を行うことができます。 ※タグは初期設定で公開になっています。詳しくはこちら |
カスタマーレビュー
|
まだカスタマーレビューはありません。
|
|||
|
ビデオレビュー
|
|
リストマニア
|
|
関連商品を探す
同じキーワードの商品を探す
フィードバック
- カタログ情報を更新するまたはイメージに対するお問い合わせ
- 著者からのコメント
- 出版社からのコメント
チェックした商品の履歴
(詳細はこちら)|
製品詳細ページやサーチ結果を表示した後、興味のあるページに戻る簡単な方法についてはここを参照してください。 |
