《大規模多媒體信息管理與檢索基礎模擬人類感知數學方法》是2011年5月1日清華大學出版社出版的圖書，作者是張智威。
基本介紹
內容簡介,作者簡介,圖書目錄,
內容簡介
大規模多媒體信息管理與檢索麵臨著兩大類艱巨的技術挑戰。首先，這一工程問題的研究在本質上是多領域、跨學科的，涉及信號處理、計算機視覺、資料庫、機器學習、神經科學和認知心理學；其次，一個有效的解決方案必須能解決高維數據和網路規模數據的可擴展性問題。這是作者在美國加州大學從事多年的教學科研及在google公司工作多年的基礎上編寫的。《大規模多媒體信息管理與檢索基礎:模擬人類感知數學方法》適合多媒體、計算機視覺、機器學習、大規模數據處理等領域的研發人員閱讀，也可作為高等院校計算機專業本科生及研究生的教材或教學參考書。
作者簡介
張智威，Dr. Edward Y. Chang was a professor at the Department of Electrical &Computer Engineering, University of California at Santa Barbara, before hejoined Google as a research director in 2006. Dr. Chang received his M.S.degree in Computer Science and Ph.D degree in Electrical Engineering,both from Stanford University.
圖書目錄
1 introduction - key subroutines of multimedia data management
1.1 overview
1.2 feature extraction
1.3 similarity
1.4 learning
1.5 multimodal fusion
1.6 indexing
1.7 scalability
1.8 concluding remarks
references
2 perceptual feature extraction
2.1 introduction
2.2 dmd algorithm
2.2.1 model-based pipeline
2.2.2 data-driven pipeline
2.3 experiments
2.3.1 dataset and setup
2.3.2 model-based vs. data-driven
2.3.3 dmd vs. individual models
2.3.4 regularization tuning
2.3.5 tough categories
2.4 related reading
2.5 concluding remarks
references
3 query concept learning
3.1 introduction
3.2 support vector machines and version space
3.3 active learning and batch sampling strategies
3.3.1 theoretical foundation
3.3.2 sampling strategies
3.4 concept-dependent learning
3.4.1 concept complexity
3.4.2 limitations of active learning
3.4.3 concept-dependent active learning algorithms
3.5 experiments and discussion
3.5.1 testbed and setup
3.5.2 active vs. passive learning
3.5.3 against traditional relevance feedback schemes
3.5.4 sampling method evaluation
3.5.5 concept-dependent learning
3.5.6 concept diversity evaluation
3.5.7 evaluation summary
3.6 related reading
3.6.1 machine learning
3.6.2 relevance feedback
3.7 relation to other chapters
3.8 concluding remarks
references
4 similarity
4.1 introduction
4.2 mining image feature set
4.2.1 image testbed setup
4.2.2 feature extraction
4.2.3 feature selection
4.3 discovering the dynamic partial distance function
4.3.1 minkowski metric and its limitations
4.3.2 dynamic partial distance function
4.3.3 psychological interpretation of dynamic partial distance function
4.4 empirical study
4.4.1 image retrieval
4.4.2 video shot-transition detection
4.4.3 near duplicated articles
4.4.4 weighted dpf vs. weighted euclidean
4.4.5 observations
4.5 related reading
4.6 concluding remarks
references
5 formulating distance functions
5.1 introduction
5.2 dfa algorithm
5.2.1 transformation model
5.2.2 distance metric learning
5.3 experimental evaluation
5.3.1 evaluation on contextual information
5.3.2 evaluation on effectiveness
5.3.3 observations
5.4 related reading
5.4.1 metric learning
5.4.2 kernel learning
5.5 concluding remarks
references
6 multimodal fusion
6.1 introduction
6.2 related reading
6.2.1 modality identification
6.2.2 modality fusion
6.3 independent modality analysis
6.3.1 pca
6.3.2 ica
6.3.3 img
6.4 super-kernel fusion
6.5 experiments
6.5.1 evaluation of modality analysis
6.5.2 evaluation of multimodal kernel fusion
6.5.3 observations
6.6 concluding remarks
references
7 fusing content and context with causality
7.1 introduction
7.2 related reading
7.2.1 photo annotation
7.2.2 probabilistic graphical models
7.3 multimodal metadata
7.3.1 contextual information
7.3.2 perceptual content
7.3.3 semantic ontology
7.4 influence diagrams
7.4.1 structure learning
7.4.2 causal strength
7.4.3 case study
7.4.4 dealing with missing attributes
7.5 experiments
7.5.1 experiment on learning structure
7.5.2 experiment on causal strength inference
7.5.3 experiment on semantic fusion
7.5.4 experiment on missing features
7.6 concluding remarks
references
8 combinational collaborative filtering, considering personalizafion
8.1 introduction
8.2 related reading
8.3 ccf: combinational collaborative filtering
8.3.1 c-u and c-d baseline models
8.3.2 ccf model
8.3.3 gibbs & em hybrid training
8.3.4 parallelization
8.3.5 inference
8.4 experiments
8.4.1 gibbs + em vs. em
8.4.2 the orkut dataset
8.4.3 runtime speedup
8.5 concluding remarks
references
9 imbalanced data learning
9.1 introduction
9.2 related reading
9.3 kernel boundary alignment
9.3.1 conformally transforming kernel k
9.3.2 modifying kernel matrix k
9.4 experimental results
9.4.1 vector-space evaluation
9.4.2 non-vector-space evaluation
9.5 concluding remarks
references
10 psvm: parallelizing support vector machines on distributed computers
10.1 introduction
10.2 interior point method with incomplete cholesky factorization
10.3 psvm algorithm
10.3.1 parallel icf
10.3.2 parallel ipm
10.3.3 computing parameter b and writing back
10.4 experiments
10.4.1 class-prediction accuracy
10.4.2 scalability
10.4.3 overheads
10.5 concluding remarks
references
11 approximate high-dimensional indexing with kernel
11.1 introduction
11.2 related reading
11.3 algorithm spheredex
11.3.1 create - building the index
11.3.2 search - querying the index
11.3.3 update - insertion and deletion
11.4 experiments
11.4.1 setup
11.4.2 performance with disk ios
11.4.3 choice of parameter g
11.4.4 impact of insertions
11.4.5 sequential vs. random
11.4.6 percentage of data processed
11.4.7 summary
11.5 concluding remarks
11.5.1 range queries
11.5.2 farthest neighbor queries
references
12 speeding up latent dirichlet allocation with parallelization and pipeline strategies
12.1 introduction
12.2 related reading
12.3 ad-lda: approximate distributed lda
12.3.1 parallel gibbs sampling and allreduce
12.3.2 mpi implementation of ad-lda
12.4 plda+
12.4.1 reduce bottleneck of ad-lda
12.4.2 framework of plda+
12.4.3 algorithm for pw processors
12.4.4 algorithm for pd processors
12.4.5 straggler handling
12.4.6 parameters and complexity
12.5 experimental results
12.5.1 datasets and experiment environment
12.5.2 perplexity
12.5.3 speedups and scalability
12.6 large-scale applications
12.6.1 mining social-network user latent behavior
12.6.2 question labeling (ql)
12.7 concluding remarks
references
