Search Results - (((((((ant OR iskantor) OR wiant) OR data) OR cantor) OR anne) OR halted) OR antiii) algorithms.

Computing the homology of the lambda algebra by Tangora, Martin C.

Table of Contents: “…Data representation""; ""4.6. The sample program""; ""4.7. …”
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Artificial neural networks in biological and environmental analysis by Hanrahan, Grady

Table of Contents: “…Data Acquisition.…”
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Developing Econometrics. by Tong, Hengqing

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Data management of protein interaction networks by Cannataro, Mario, 1964-

Table of Contents: “…Frontmatter -- Color Plates -- Interactomics -- Technologies for Discovering Protein Interactions -- Graph Theory and Applications -- Protein-To-Protein Interaction Data -- Protein-To-Protein Interaction DATABASES -- Models for Protein Interaction Networks -- Algorithms Analyzing Features of Protein Interaction Networks -- Algorithms Comparing Protein Interaction Networks -- Ontology-Based Analysis of Protein Interaction Networks -- Visualization of Protein Interaction Networks -- Case Studies in Biology and Bioinformatics -- Future Trends -- References -- Index.…”
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Sharing economy and big data analytics by Sedkaoui, Soraya, Khelfaoui, Mounia

Table of Contents: “…Not Without Big Data Algorithms. Linear Regression -- Classification Algorithms -- Cluster Analysis -- Conclusion -- References -- Index -- Other titles from iSTE in Information Systems, Web and Pervasive Computing.…”
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Multilinear subspace learning : dimensionality reduction of multidimensional data by Lu, Haiping, Plataniotis, Konstantinos N., Venetsanopoulos, A. N. (Anastasios N.), 1941-

Table of Contents: “…Introduction -- Fundamentals and Foundations -- Linear Subspace Learning for Dimensionality Reduction -- Fundamentals of Multilinear Subspace Learning -- Overview of Multilinear Subspace Learning -- Algorithmic and Computational Aspects -- Algorithms and Applications -- Multilinear Principal Component Analysis -- Multilinear Discriminant Analysis -- Multilinear ICA, CCA, and PLS -- Applications of Multilinear Subspace Learning -- Appendix A: Mathematical Background -- Appendix B: Data and Preprocessing -- Appendix C: Software.…”
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Machine learning in image steganalysis by Schaathun, Hans Georg

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Big Data, IoT, and Machine Learning : Tools and Applications. by Agrawal, Rashmi

Table of Contents: “…Chapter 3 Reviews Analysis of Apple Store Applications Using Supervised Machine Learning -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 Machine Learning Algorithms -- 3.2.2 Feature Extraction Algorithms -- 3.3 Proposed Methodology -- 3.3.1 Data Collection -- 3.3.2 Feature Extraction -- 3.3.3 Data Analysis and Sentiment Analysis -- Text Processing -- 3.3.4 Text Normalisation -- 3.4 Feature Extraction Algorithm -- 3.4.1 CountVectorizer -- 3.4.2 TfidfVectorizer (TF-IDF) -- 3.5 Supervised ML Classification -- 3.6 Experiment Design -- 3.7 Experimental Results and Analysis…”
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Statistical methods of geophysical data processing by Troi︠a︡n, V. N. (Vladimir Nikolaevich)

Table of Contents: “…The method of the separation of interfering signals -- 8. Algorithms of approximation of geophysical data. 8.1. …”
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Entity information life cycle for big data : master data management and information integration by Talburt, John R., Zhou, Yinle, 1986-

Table of Contents: “…CLUSTER-TO-CLUSTER CLASSIFICATIONSELECTING AN APPROPRIATE ALGORITHM; CONCLUDING REMARKS; Chapter 9 -- Blocking; BLOCKING; BLOCKING BY MATCH KEY; DYNAMIC BLOCKING VERSUS PRERESOLUTION BLOCKING; BLOCKING PRECISION AND RECALL; MATCH KEY BLOCKING FOR BOOLEAN RULES; MATCH KEY BLOCKING FOR SCORING RULES; CONCLUDING REMARKS; Chapter 10 -- CSRUD for Big Data; LARGE-SCALE ER FOR MDM; THE TRANSITIVE CLOSURE PROBLEM; DISTRIBUTED, MULTIPLE-INDEX, RECORD-BASED RESOLUTION; AN ITERATIVE, NONRECURSIVE ALGORITHM FOR TRANSITIVE CLOSURE; ITERATION PHASE: SUCCESSIVE CLOSURE BY REFERENCE IDENTIFIER.…”
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Fuzzy systems and data mining II : proceedings of FSDM 2016

Table of Contents: “…Adaptive Fuzzy Sliding-Mode Control of Robot and SimulationHesitant Bipolar Fuzzy Set and Its Application in Decision Making; Chance Constrained Twin Support Vector Machine for Uncertain Pattern Classification; Set-Theoretic Kripke-Style Semantics for Monoidal T-Norm (Based) Logics; Data Mining; Dynamic Itemset Mining Under Multiple Support Thresholds; Deep Learning with Large Scale Dataset for Credit Card Data Analysis; Probabilistic Frequent Itemset Mining Algorithm over Uncertain Databases with Sampling; Priority Guaranteed and Energy Efficient Routing in Data Center Networks.…”
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Biomedical data mining for information retrieval : methodologies, techniques, and applications

Table of Contents: “…Mortality Prediction of ICU Patients Using Machine Learning Techniques / Babita Majhi, Aarti Kashyap, Ritanjali Majhi -- Artificial Intelligence in Bioinformatics / VSamuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey, Archana Gupta, Arpana Vibhuti -- Predictive Analysis in Healthcare Using Feature Selection / Aneri Acharya, Jitali Patel, Jigna Patel -- Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications / Deepanshu Bajaj, Bharat Bhushan, Divya Yadav -- Improved Social Media Data Mining for Analyzing Medical Trends / Minakshi Sharma, Sunil Sharma -- Bioinformatics: An Important Tool in Oncology / Gaganpreet Kaur, Saurabh Gupta, Gagandeep Kaur, Manju Verma, Pawandeep Kaur -- Biomedical Big Data Analytics Using IoT in Health Informatics / Pawan Singh Gangwar, Yasha Hasija -- Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline / Anusuya Pal, Amalesh Gope, Germano S Iannacchione -- Introduction to Deep Learning in Health Informatics / Monika Jyotiyana, Nishtha Kesswani -- Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review / Shikha Gupta, Nitish Mehndiratta, Swarnim Sinha, Sangana Chaturvedi, Mehak Singla -- Deep Learning Applications in Medical Image Analysis / Ananya Singha, Rini Smita Thakur, Tushar Patel -- Role of Medical Image Analysis in Oncology / Kaur Gaganpreet, Garg Hardik, Kumari Heena, Lakhvir Singh, Navroz Kaur, Shubham Kumar, Shadab Alam -- A Comparative Analysis of Classifiers Using Particle Swarm Optimization-Based Feature Selection / Chandra Sekhar Biswal, Subhendu Kumar Pani, Sujata Dash.…”
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Least squares data fitting with applications by Hansen, Per Christian

Table of Contents: “…s method -- 9.2 The Gauss-Newton method -- 9.3 The Levenberg-Marquardt method -- 9.4 Additional considerations and software -- 9.5 Iteratively reweighted LSQ algorithms for robust data fitting problems -- 9.6 Variable projection algorithm -- 9.7 Block methods for large-scale problems -- 10 Ill-Conditioned Problems…”
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Reliability assurance of big data in the cloud : cost-effective replication-based storage by Li, Wenhao, Yang, Yun (University lecturer), Yuan, Dong

Table of Contents: “…1.1. Data reliability in the Cloud -- 1.2. Background of Cloud storage -- 1.2.1. …”
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Applied computational economics and finance by Miranda, Mario J., Fackler, Paul L.

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Nonlinear integrals and their applications in data mining by Wang, Zhenyuan

Table of Contents: “…Advanced consideration on the nonlinear classification involving the Choquet integral -- ch. 11. Data mining with fuzzy data. 11.1. Defuzzified Choquet Integral with Fuzzy-Valued Integrand (DCIFI). 11.2. …”
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Corpus-based and computational approaches to discourse anaphora

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Cognitive computing and big data analytics by Hurwitz, Judith, Kaufman, Marcia, Bowles, Adrian, 1952-

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Applied mathematics for the analysis of biomedical data : models, methods, and MATLAB by Costa, Peter J.

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Relative fidelity processing of seismic data : methods and applications by Wang, Xiwen, 1956-

Table of Contents: “…Title Page ; Copyright Page; Contents; Preface; Chapter 1 Study on Method for Relative Fidelity Preservation of Seismic Data; 1.1 Introduction; 1.2 Discussion on Impact on Processing of High-resolution, High SNR for Seismic Acquisition and Observation Mode; 1.3 Discussion on the Cause of Notching; 1.4 Discussion of Impact on Processing of Relative Fidelity Preservation Seismic Data for Seismic Acquisition and Observation Mode; 1.5 Comparison of Results of High-resolution, High SNR Processing and Relative Fidelity Preservation Processing ; 1.6 Elastic Wave Forward Modeling; 1.7 Conclusions.…”
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