免费下载书籍地址：PDF下载地址
精美图片
信息时代的计算机科学理论 (美)霍普克罗夫特 (美)坎南 著作书籍详细信息
- ISBN：9787313096098
- 作者：暂无作者
- 出版社：暂无出版社
- 出版时间：2013-05
- 页数：暂无页数
- 价格：23.80
- 纸张：轻型纸
- 装帧：平装-胶订
- 开本：16开
- 语言：未知
- 丛书：暂无丛书
- TAG：暂无
- 豆瓣评分：暂无豆瓣评分
寄语：
新华书店正版，关注店铺成为会员可享店铺专属优惠，团购客户请咨询在线客服！
内容简介：
《信息时代的计算机科学理论(英文版)》是交大致远教材系列之一，由约翰？霍普罗夫特编著。
《信息时代的计算机科学理论(英文版)》简介：
ComputerScienceTheoryfortheInformationAgecoversthecomputersciencetheorylikelytobeusefulinthenext40years,includinghigh-dimensionalspace,randomgraphs,singularvaluedecomposition.randomwalks,Markovchains,learningalgorithms,VC-dimension,algorithmsformassivedateproblems,clustering.Thebookalsocoversgraphicalmodelsandbeliefpropagation,rankingandvoting,sparsevectors,andcompressedsensing.
Thebookisintendedforeitheranundergraduateoragraduatetheorycourseincomputerscience.
Prof.JohnHopcroftisaworld-renownedscientistandanexpertoneducationincomputerscience.HewasawardedtheA.M.TuringAwardin1986forhiscontributionsintheoreticalcomputinganddatastructuredesign.Dr.RavindranKannanisaprincipalresearcherwithMicrosoftResearchLabslocatedinIndia.
书籍目录：
1 Introduction
2 High-Dimensional Space
2.1 Properties of High-Dimensional Space
2.2 The High-Dimensional Sphere
2.2.1 The Sphere and the Cube in Higher Dimensions
2.2.2 Volume and Surface Area of the Unit Sphere
2.2.3 The Volume is Near the Equator
2.2.4 The Volume is in a Narrow Annulus
2.2.5 The Surface Area is Near the Equator
2.3 Volumes of Other Solids
2.4 Generating Points Uniformly at Random on the Surface of a Sphere
2.5 Gaussians in High Dimension
2.6 Bounds on Tail Probability
2.7 Random Projection and the Johnson-Lindenstrauss Theorem
2.8 Bibliographic Notes
2.9 Exercises
3 Random Graphs
3.1 TheG(n, p) Model
3.1.1 Degree Distribution
3.1.2 Existence of Triangles in G ( n, d
)
3.2 Phase Transitions
3.3 The Giant Component
3.4 Branching Processes
3.5 Cycles and Full Connectivity
3.5.1 Emergence of Cycles
3.5.2 Full Connectivity
3.5.3 Threshold for O (Inn) Diameter
3.6 Phase Transitions for Monotone Properties
3.7 Phase Transitions for CNF-sat
3.8 Nonuniform and Growth Models of Random Graphs
3.8.1 Nonuniform Models
3.8.2 Giant Component in Random Graphs with Given Degree Distribution ...
3.9 Growth Models
3.9.1 Growth Model Without Preferential Attachment
3.9.2 A Growth Model with Preferential Attachment
3.10 Small World Graphs
3.11 Bibliographic Notes
3.12 Exercises
4 Singular Value Decomposition (SVD)
4.1 Singular Vectors
4.2 Singular Value Decomposition (SVD)
4.3 Best Rank k Approximations
4.4 Power Method for Computing the Singular Value Decomposition
4.5 Applications of Singular Value Decomposition
4.5.1 Principal Component Analysis
4.5.2 Clustering a Mixture of Spherical Gaussians
4.5.3 An Application of SVD to a Discrete Optimization Problem
4.5.4 Spectral Decomposition
4.5.5 Singular Vectors and Ranking Documents
4.6 Bibliographic Notes
4.7 Exercises
5 Random Walks and Markov Chains
5.1 Stationary Distribution
5.2 Electrical Networks and Random Walks
5.3 Random Walks on Undirected Graphs with Unit Edge Weights
5.4 Random Walks in Euclidean Space
5.5 The Web as a Markov Chain
5.6 Markov Chain Monte Carlo
5.6.1 Metropolis-Hasting Algorithm
5.6.2 Gibbs Sampling
5.7 Convergence of Random Walks on Undirected Graphs
5.7.1 Using Normalized Conductance to Prove Convergence
5.8 Bibliographic Notes
5.9 Exercises
6 Learning and VC-Dimension
6.1 Learning
6.2 Linear Separators, the Perceptron Algorithm, and Margins
6.3 Nonlinear Separators, Support Vector Machines, and Kernels
6.4 Strong and Weak Learning-Boosting
6.5 Number of Examples Needed for Prediction: VC-Dimension
6.6 Vapnik-Chervonenkis or VC-Dimension
6.6.1 Examples of Set Systems and Their VC-Dimension
6.6.2 The Shatter Function
6.6.3 Shatter Function for Set Systems of Bounded VC-Dimension
6.6.4 Intersection Systems
6.7 The VC Theorem
6.8 Bibliographic Notes
6.9 Exercises
7 Algorithms for Massive Data Problems
7.1 Frequency Moments of Data Streams
7.1.1 Number of Distinct Elements in a Data Stream
7.1.2 Counting the Number of Occurrences of a Given Element
7.1.3 Counting Frequent Elements
7.1.4 The Second Moment
7.2 Sketch of a Large Matrix
7.2.1 Matrix Multiplication Using Sampling
7.2.2 Approximating a Matrix with a Sample of Rows and Columns ...
7.3 Sketches of Documents
7.4 Exercises
8 Clustering
8.1 Some Clustering Examples
8.2 A Simple Greedy Algorithm for k-clustering
8.3 Lloyd's Algorithm for k-means Clustering
8.4 Meaningful Clustering via Singular Value Decomposition
8.5 Recursive Clustering Based on Sparse Cuts
8.6 Kernel Methods
8.7 Agglomerative Clustering
8.8 Communities, Dense Submatrices
8.9 Flow Methods
8.10 Linear Programming Formulation
8.11 Finding a Local Cluster Without Examining the Whole Graph
8.12 Axioms for Clustering
8.12.1 An Impossibility Result
8.12.2 A Satisfiable Set of Axioms
8.13 Exercises
9 Graphical Models and Belief Propagation
9.1 Bayesian or Belief Networks
9.2 Markov Random Fields
9.3 Factor Graphs
9.4 Tree Algorithms
9.5 Message Passing Algorithm
9.6 Graphs with a Single Cycle
9.7 Belief Update in Networks with a Single Loop
9.8 Maximum Weight Matching
9.9 Warning Propagation
9.10 Correlation Between Variables
9.11 Exercises
10 Other Topics
10.1 Rankings
10.2 Hare System for Voting
10.3 Compressed Sensing and Sparse Vectors
10.3.1 Unique Reconstruction of a Sparse Vector
10.3.2 The Exact Reconstruction Property
10.3.3 Restricted Isometry Property
10.4 Applications
10.4.1 Sparse Vector in Some Coordinate Basis
10.4.2 A Representation Cannot be Sparse in Both Time and Frequency Domains
10.4.3 Biological
10.4.4 Finding Overlapping Cliques or Communities
10.4.5 Low Rank Matrices
10.5 Exercises
11 Appendix
11.1 Asymptotic Notation
11.2 Useful Inequalities
11.3 Sums of Series
11.4 Probability
11.4.1 Sample Space, Events, Independence
11.4.2 Variance
11.4.3 Variance of Sum of Independent Random Variables
11.4.4 Covariance
11.4.5 The Central Limit Theorem
11.4.6 Median
11.4.7 Unbiased Estimators
11.4.8 Probability Distributions
11.4.9 Maximum Likelihood Estimation MLE
11.4.10 Tail Bounds
11.4.11 Chernoff Bounds: Bounding of Large Deviations
11.4.12 Hoeffding's Inequality
11.5 Generating Functions
11.5.1 Generating Functions for Sequences Defined by Recurrence Relationships
11.5.2 Exponential Generating Function
11.6 Eigenvalues and Eigenvectors
11.6.1 Eigenvalues and Eigenvectors
11.6.2 Symmetric Matrices
11.6.3 Extremal Properties of Eigenvalues
11.6.4 Eigenvalues of the Sum of Two Symmetric Matrices
11.6.5 Norms
11.6.6 Important Norms and Their Properties
11.6.7 Linear Algebra
11.6.8 Distance Between Subspaces
11.7 Miscellaneous
11.7.1 Variational Methods
11.7.2 Hash Functions
11.7.3 Catalan Numbers
11.7.4 Sperner's Lemma
11.8 Exercises
Index
References
作者介绍：
暂无相关内容，正在全力查找中
出版社信息：
暂无出版社相关信息，正在全力查找中！
书籍摘录：
暂无相关书籍摘录，正在全力查找中！
在线阅读/听书/购买/PDF下载地址：
在线阅读地址：信息时代的计算机科学理论 (美)霍普克罗夫特 (美)坎南 著作在线阅读
在线听书地址：信息时代的计算机科学理论 (美)霍普克罗夫特 (美)坎南 著作在线收听
在线购买地址：信息时代的计算机科学理论 (美)霍普克罗夫特 (美)坎南 著作在线购买
原文赏析：
暂无原文赏析，正在全力查找中！
其它内容：
暂无其它内容！
书籍真实打分
故事情节：4分
人物塑造：3分
主题深度：9分
文字风格：5分
语言运用：3分
文笔流畅：7分
思想传递：3分
知识深度：6分
知识广度：8分
实用性：5分
章节划分：4分
结构布局：8分
新颖与独特：6分
情感共鸣：8分
引人入胜：6分
现实相关：5分
沉浸感：8分
事实准确性：4分
文化贡献：4分
网站评分
书籍多样性：4分
书籍信息完全性：9分
网站更新速度：3分
使用便利性：4分
书籍清晰度：8分
书籍格式兼容性：5分
是否包含广告：8分
加载速度：8分
安全性：3分
稳定性：9分
搜索功能：4分
下载便捷性：9分
下载点评
- 体验满分(408+)
- 一星好评(575+)
- 简单(77+)
- 图书多(428+)
- 书籍完整(399+)
- 可以购买(666+)
- 速度慢(385+)
- 内涵好书(629+)
- 无漏页(225+)
- 种类多(667+)
下载评价
网友 后***之：强烈推荐！无论下载速度还是书籍内容都没话说 真的很良心！
网友 菱***兰：特好。有好多书
网友 曾***文：五星好评哦
网友 寇***音：好，真的挺使用的！
网友 隗***杉：挺好的，还好看！支持！快下载吧！
网友 谭***然：如果不要钱就好了
网友 权***波：收费就是好，还可以多种搜索，实在不行直接留言，24小时没发到你邮箱自动退款的！
网友 康***溪：强烈推荐！！！
网友 师***怀：好是好，要是能免费下就好了
网友 田***珊：可以就是有些书搜不到
网友 孙***美：加油！支持一下！不错，好用。大家可以去试一下哦
网友 通***蕊：五颗星、五颗星，大赞还觉得不错！~~