由于此商品库存有限,请在下单后15分钟之内支付完成,手慢无哦!
100%刮中券,最高99元无敌券,券有效期7天
活动自2017年6月2日上线,敬请关注云钻刮券活动规则更新。
如活动受政府机关指令需要停止举办的,或活动遭受严重网络攻击需暂停举办的,或者系统故障导致的其它意外问题,苏宁无需为此承担赔偿或者进行补偿。
正版 基于回归视野的统计学习 (美)R.A.伯克 世界图书出版公司
¥ ×1
Preface
1 Statistical Learning as a Regression Problem
1.1 Getting Started
1.2 Setting the Regression Context
1.3 The Transition to Statistical Learning
1.3.1 Some Goals of Statistical Learning
1.3.2 Statistical Inference
1.3.3 Some Initial Cautions
1.3.4 A Cartoon Illustration
1.3.5 A Taste of Things to Come
1.4 Some Initial Concepts and Definitions
1.4.1 Overall Goals
1.4.2 Loss Functions and Related Concepts
1.4.3 Linear Estimators
1.4.4 Degrees of Freedom
1.4.5 Model Evaluation
1.4.6 Model Selection
1.4.7 Basis Functions
1.5 Some Common Themes
1.6 Summary and Conclusions
2 Regression Splines and Regression Smoothers
2.1 Introduction
2.2 Regression Splines
2.2.1 Applying a Piecewise Linear Basis
2.2.2 Polynomial Regression Splines
2.2.3 Natural Cubic Splines
2.2.4 B-Splines
2.3 Penalized Smoothing
2.3.1 Shrinkage
2.3.2 Shrinkage and Statistical Inference
2.3.3 Shrinkage: So What?
2.4 Smoothing Splines
2.4.1 An Illustration
2.5 Locally Weighted Regression as a Smoother
2.5.1 Nearest Nei***or Methods
2.5.2 Locally Weighted Regression
2.6 Smoothers for Multiple Predictors
2.6.1 Smoothing in Two Dimensions
2.6.2 The Generalized Additive Model
2.7 Smoothers with Categorical Variables
2.7.1 An Illustration
2.8 Locally Adaptive Smoothers
2.9 The Role of Statistical Inference
2.9.1 Some Apparent Prerequisites
2.9.2 Confidence Intervals
2.9.3 Statistical Tests
2.9.4 Can Asymptotics Help?
2.10 Software Issues
2.11 Summary and Conclusions
3 Classification and Regression Trees (CART)
R.A.伯克,宾夕法尼亚大学数理统计系教授,研究领域广泛,在社会科学和自然科学均有很深的造诣。
《基于回归视野的统计学习》作者是宾夕法尼亚大学数理统计系教授,研究领域广泛,在社会科学和自然科学均有很深的造诣。本书主要阐述统计学习的应用知识,各章还有实际应用实例,可作为统计、社会科学和生命科学等相关领域的研究生和科研人员的参考书。
亲,大宗购物请点击企业用户渠道>小苏的服务会更贴心!
亲,很抱歉,您购买的宝贝销售异常火爆让小苏措手不及,请稍后再试~
非常抱歉,您前期未参加预订活动,
无法支付尾款哦!
抱歉,您暂无任性付资格
