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Synopsis
本书对所有主要的机器学习方法和新研究趋势进行了深入探索，涵盖概率和确定性方法以及贝叶斯推断方法。其中，经典方法包括平均/小二乘滤波、卡尔曼滤波、*逼近和在线学习、贝叶斯分类、决策树、逻辑回归和提升方法等，新趋势包括稀疏、凸分析与优化、在线分布式算法、RKH空间学习、贝叶斯推断、图模型与隐马尔可夫模型、粒子滤波、深度学习、字典学习和潜变量建模等。全书构建了一套明晰的机器学习知识体系，各章内容相对独立，物理推理、数学建模和算法实现精准且细致，并辅以应用实例和习题。本书适合该领域的科研人员和工程师阅读，也适合学习模式识别、统计/自适应信号处理和深度学习等课程的学生参考。
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- Publisher机械工业出版社
- Publication date2017
- ISBN 10 7111565266
- ISBN 13 9787111565260
- BindingPaperback
- LanguageChinese
- Edition number1
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Machine learning: Bayesian and Optimization Methods (English version)(Chinese Edition)
ISBN 10: 7111565266
ISBN 13: 9787111565260
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paperback
Seller: liu xing, Nanjing, JS, China
Seller rating 5 out of 5 stars
paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2017-05-01 Pages: $number Publisher: Machinery Industry Press This book explores all major machine learning methods and new research trends. including probabilistic and deterministic methods and Bayesian inference methods. Among them. the classical methods include average small square filter. Kalman filter. stochastic approximation and online learning. Bayesian classification. decision . Seller Inventory # DP048942
Quantity: 3 available