04215nam 22006135 450 991111334560332120250723130320.03-031-92517-310.1007/978-3-031-92517-7(CKB)39698323200041(MiAaPQ)EBC32231112(Au-PeEL)EBL32231112(DE-He213)978-3-031-92517-7(OCoLC)1531326646(EXLCZ)993969832320004120250723d2026 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierCloud Computing 12th EAI International Conference, CloudComp 2024, Luton, UK, September 9–10, 2024, Proceedings /edited by Xiaohua Feng, Patrick Siarry, Liangxiu Han, Longzhi Yang1st ed. 2026.Cham :Springer Nature Switzerland :Imprint: Springer,2026.1 online resource (306 pages)Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering,1867-822X ;6173-031-92516-5 The Cloud-Edging Computing -- Admission Control and Scheduling of Offloaded Tasks Over Mobile Edge Servers -- Identifying the ‘gaps’ where Cloud Computing can help Computer Laws in curbing Cyber bullying and Cyber stalking -- SecureCloud: A Cross-Platform Encrypted File Sharing Solution with Forensic Imaging Capability in India -- Integration of AI and Cloud Computing: Advantages and Challenges -- Understanding Human Behavior through Smart Home IoT Data Analysis: Patterns and Insights -- Blockchain in Space-Air-Ground Integrated Networks: A Critical Look -- Empowering HEIs through LLMs and Cloud Computing: Strategies for Seamless Integration and Sustainable Transformation -- Wireless Networks; Network Security -- Network Intrusion Detection by Adaptive Deep Metric Learning -- AI, Wireless Security and Forensics with Cloud, Networks Impact on Education -- Lightweight CSI Feedback with Global Context Attention for RIS-Assisted Communications -- Discriminative Features Learning Based Approach for Object Detection Enhancement -- Emerging Applications /The Cloud-Edging Integration Applications -- An LLM-based Agent Framework for Dynamic and Semantic Data Fusion, Integration and Engineering for Data Analysis -- AI for Law and AI under Law in China -- Enhancing Biometric Security: Advancements in Environment-Independent Channel State Information Analysis -- Monitoring Patient Apps Security Vulnerabilities -- Leveraging Artificial Intelligence Integration in Regional Sports Education to Foster Equitable and Sustainable Development.This book LNICST 617 constitutes the refereed proceedings of the 12th EAI International Conference on Cloud Computing, CloudComp 2024, held in Luton, UK, during September 9–10, 2024. The 16 full papers were carefully reviewed and selected from 42 submissions. The proceedings focus on topics such as The Cloud-Edging Computing Wireless Networks; Network Security Emerging Applications /The Cloud-Edging Integration Applications.Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering,1867-822X ;617Computer networksApplication softwareComputers, Special purposeComputer Communication NetworksComputer and Information Systems ApplicationsSpecial Purpose and Application-Based SystemsComputer networks.Application software.Computers, Special purpose.Computer Communication Networks.Computer and Information Systems Applications.Special Purpose and Application-Based Systems.004.6782Feng Xiaohua2001666Siarry Patrick860327Han Liangxiu2001667Yang Longzhi2001668MiAaPQMiAaPQMiAaPQBOOK9911113345603321Cloud Computing4785180UNINA05635nam 2200745 a 450 991115031900332120251117074605.097866127578539781282757851128275785797898142710739814271071(CKB)2490000000001739(EBL)1679487(OCoLC)859886714(SSID)ssj0000424957(PQKBManifestationID)11306088(PQKBTitleCode)TC0000424957(PQKBWorkID)10476709(PQKB)10330674(MiAaPQ)EBC1679487(WSP)00000652(Au-PeEL)EBL1679487(CaPaEBR)ebr10422182(CaONFJC)MIL275785(Perlego)849088(EXLCZ)99249000000000173920100520d2010 uy 0engurcn|||||||||txtccrPattern classification using ensemble methods /Lior Rokach1st ed.Singapore ;Hackensack, NJ World Scientificc20101 online resource (242 p.)Series in machine perception and artificial intelligence ;v. 75Description based upon print version of record.9789814271066 9814271063 Includes bibliographical references (p. 185-222) and index.Contents; Preface; 1. Introduction to Pattern Classification; 1.1 Pattern Classification; 1.2 Induction Algorithms; 1.3 Rule Induction; 1.4 Decision Trees; 1.5 Bayesian Methods; 1.5.1 Overview.; 1.5.2 Naıve Bayes; 1.5.2.1 The Basic Naıve Bayes Classifier; 1.5.2.2 Naıve Bayes Induction for Numeric Attributes; 1.5.2.3 Correction to the Probability Estimation; 1.5.2.4 Laplace Correction; 1.5.2.5 No Match; 1.5.3 Other Bayesian Methods; 1.6 Other Induction Methods; 1.6.1 Neural Networks; 1.6.2 Genetic Algorithms; 1.6.3 Instance-based Learning; 1.6.4 Support Vector Machines2. Introduction to Ensemble Learning 2.1 Back to the Roots; 2.2 The Wisdom of Crowds; 2.3 The Bagging Algorithm; 2.4 The Boosting Algorithm; 2.5 The Ada Boost Algorithm; 2.6 No Free Lunch Theorem and Ensemble Learning; 2.7 Bias-Variance Decomposition and Ensemble Learning; 2.8 Occam's Razor and Ensemble Learning; 2.9 Classifier Dependency; 2.9.1 Dependent Methods; 2.9.1.1 Model-guided Instance Selection; 2.9.1.2 Basic Boosting Algorithms; 2.9.1.3 Advanced Boosting Algorithms; 2.9.1.4 Incremental Batch Learning; 2.9.2 Independent Methods; 2.9.2.1 Bagging; 2.9.2.2 Wagging2.9.2.3 Random Forest and Random Subspace Projection 2.9.2.4 Non-Linear Boosting Projection (NLBP); 2.9.2.5 Cross-validated Committees; 2.9.2.6 Robust Boosting; 2.10 Ensemble Methods for Advanced Classification Tasks; 2.10.1 Cost-Sensitive Classification; 2.10.2 Ensemble for Learning Concept Drift; 2.10.3 Reject Driven Classification; 3. Ensemble Classification; 3.1 Fusions Methods; 3.1.1 Weighting Methods; 3.1.2 Majority Voting; 3.1.3 Performance Weighting; 3.1.4 Distribution Summation; 3.1.5 Bayesian Combination; 3.1.6 Dempster-Shafer; 3.1.7 Vogging; 3.1.8 Naıve Bayes3.1.9 Entropy Weighting 3.1.10 Density-based Weighting; 3.1.11 DEA Weighting Method; 3.1.12 Logarithmic Opinion Pool; 3.1.13 Order Statistics; 3.2 Selecting Classification; 3.2.1 Partitioning the Instance Space; 3.2.1.1 The K-Means Algorithm as a Decomposition Tool; 3.2.1.2 Determining the Number of Subsets; 3.2.1.3 The Basic K-Classifier Algorithm; 3.2.1.4 The Heterogeneity Detecting K-Classifier (HDK-Classifier); 3.2.1.5 Running-Time Complexity; 3.3 Mixture of Experts and Meta Learning; 3.3.1 Stacking; 3.3.2 Arbiter Trees; 3.3.3 Combiner Trees; 3.3.4 Grading; 3.3.5 Gating Network4. Ensemble Diversity 4.1 Overview; 4.2 Manipulating the Inducer; 4.2.1 Manipulation of the Inducer's Parameters; 4.2.2 Starting Point in Hypothesis Space; 4.2.3 Hypothesis Space Traversal; 4.3 Manipulating the Training Samples; 4.3.1 Resampling; 4.3.2 Creation; 4.3.3 Partitioning; 4.4 Manipulating the Target Attribute Representation; 4.4.1 Label Switching; 4.5 Partitioning the Search Space; 4.5.1 Divide and Conquer; 4.5.2 Feature Subset-based Ensemble Methods; 4.5.2.1 Random-based Strategy; 4.5.2.2 Reduct-based Strategy; 4.5.2.3 Collective-Performance-based Strategy4.5.2.4 Feature Set PartitioningResearchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptionsSeries in machine perception and artificial intelligence ;v. 75.Pattern recognition systemsAlgorithmsMachine learningPattern recognition systems.Algorithms.Machine learning.621.389/28Rokach Lior620362MiAaPQMiAaPQMiAaPQBOOK9911150319003321Pattern classification using ensemble methods4865911UNINA