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Jingdong book

计算机科学丛书：人工智能·智能系统指南（原书第3版）

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Jingdong book

计算机科学丛书：人工智能·智能系统指南（原书第3版）

Name: 计算机科学丛书：人工智能·智能系统指南（原书第3版）
Brand: Jingdong book
SKU: 3118115551
Price: 31.68 USD
Availability: OutOfStock

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Content Description

《计算机科学丛书：人工智能·智能系统指南（原书第3版）》是一本很好的人工智能入门书籍,内容丰富、浅显易懂。作者根据自己多年的教学、实践经验，并结合实际代码、图示、案例等讲解了人工智能的基本知识。
全书共分10章，主要内容包括：基于规则的专家系统、不确定性管理技术、模糊专家系统、基于框架的专家系统、人工神经网络、进化计算、混合智能系统、知识工程、数据挖掘等。另外，本书还提供了一个人工智能相关术语表和包含商业化的人工智能工具的附录。
《计算机科学丛书：人工智能·智能系统指南（原书第3版）》既可以作为计算机科学相关专业本科生的入门教材，也可以作为非计算机科学专业读者的自学参考书。

Author Description

作者：（澳大利亚）尼格尼维斯基（Michael Negnevitsky）译者：陈薇

Catalogue

译者序
第3版前言
第1版前言
本书概要
致谢
第1章基于知识的智能系统概述
1.1 智能机
1.2 人工智能的发展历史，从“黑暗时代”到基于知识的系统
1.2.1 “黑暗时代”，人工智能的诞生（1943年~1956年）
1.2.2 人工智能的上升期，远大目标积极实现的年代（1956年～20世纪60年代晚期）
1.2.3 没有履行的诺言，来自现实的冲击（20世纪60年代晚期～20世纪70年代早期）
1.2.4 专家系统技术，成功的关键因素（20世纪70年代早期～20世纪80年代中期）
1.2.5 如何使机器学习，神经网络的重生（20世纪80年代中期至今）
1.2.6 进化计算，在尝试中学习（20世纪70年代早期至今）
1.2.7 知识工程的新纪元，文字计算（20世纪80年代后期至今）
1.3 小结
复习题
参考文献

第2章基于规则的专家系统
2.1 知识概述
2.2 知识表达技术——规则
2.3 专家系统研发团队的主要参与者
2.4 基于规则的专家系统的结构
2.5 专家系统的基本特征
2.6 前向链接和后向链接推理技术
2.6.1 前向链接
2.6.2 后向链接
2.7 MEDIA ADVISOR：基于规则的专家系统实例
2.8 冲突消解
2.9 基于规则的专家系统的优点和缺点
2.1 0小结
复习题
参考文献

第3章基于规则的专家系统中的不确定性管理
3.1 不确定性简介
3.2 概率论基本知识
3.3 贝叶斯推理
3.4 FORECAST：论据累积的贝叶斯方法
3.5 贝叶斯方法的偏差
3.6 确信因子理论和基于论据的推理
3.7 FORECAST：确信因子的应用
3.8 贝叶斯推理和确信因子的对比
3.9 小结
复习题
参考文献

第4章模糊专家系统
4.1 概述
4.2 模糊集
4.3 语言变量和模糊限制语
4.4 模糊集的操作
4.5 模糊规则
4.6 模糊推理
4.6.1 Mamdanistyle 推理
4.6.2 Sugenostyle推理
4.7 建立模糊专家系统
4.8 小结
复习题
参考文献
参考书目

第5章基于框架的专家系统
5.1 框架简介
5.2 知识表达技术——框架
5.3 基于框架的系统中的继承
5.4 方法和守护程序
5.5 框架和规则的交互
5.6 基于框架的专家系统实例：Buy Smart
5.7 小结
复习题
参考文献
参考书目

第6章人工神经网络
6.1 人脑工作机制简介
6.2 作为简单计算元素的神经元
6.3 感知器
6.4 多层神经网络
6.5 多层神经网络的加速学习
6.6 Hopfield网络
6.7 双向联想记忆
6.8 自组织神经网络
6.8.1 Hebbian学习
6.8.2 竞争学习
6.9 小结
复习题
参考文献

第7章进化计算
7.1 进化是智能的吗
7.2 模拟自然进化
7.3 遗传算法
7.4 遗传算法为什么可行
7.5 案例研究：用遗传算法来维护调度
7.6 进化策略
7.7 遗传编程
7.8 小结
复习题
参考文献
参考书目

第8章混合智能系统
8.1 概述
8.2 神经专家系统
8.3 神经-模糊系统
8.4 ANFIS
8.5 进化神经网络
8.6 模糊进化系统
8.7 小结
复习题
参考文献

第9章知识工程
9.1 知识工程简介
9.1.1 问题评估
9.1.2 数据和知识获取
9.1.3 原型系统开发
9.1.4 完整系统开发
9.1.5 系统评价和修订
9.1.6 系统集成和维护
9.2 专家系统可以解决的问题
9.3 模糊专家系统可以解决的问题
9.4 神经网络可以解决的问题
9.5 遗传算法可以解决的问题
9.6 混合智能系统可以解决的问题
9.7 小结
复习题
参考文献

第10章数据挖掘和知识发现
10.1 数据挖掘简介
10.2 统计方法和数据可视化
10.3 主成分分析
10.4 关系数据库和数据库查询
10.5 数据仓库和多维数据分析
10.6 决策树
10.7 关联规则和购物篮分析
10.8 小结
复习题
参考文献
术语表
附录人工智能工具和经销商
索引

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