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1. First-order and Stochastic Optimization Methods for Machine Learning. 1st ed. 2020
by Guanghui Lan
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Springer Series in the Data Sciences ;
オンライン: https://doi.org/10.1007/978-3-030-39568-1
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Machine Learning Models
Convex Optimization Theory
Deterministic Convex Optimization
Stochastic Convex Optimization
Convex Finite-sum and Distributed Optimization
Nonconvex Optimization
Projection-free Methods
Operator Sliding and Decentralized Optimization
Machine Learning Models
Convex Optimization Theory
Deterministic Convex Optimization
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2. Embracing Industry 4.0 : Selected Articles from MUCET 2019. 1st ed. 2020
edited by Mohd Azraai Mohd Razman, Jessnor Arif Mat Jizat, Nafrizuan Mat Yahya, Hyun Myung, Amar Faiz Zainal Abidin, Mohamad Shaiful Abdul Karim
出版情報: Singapore : Springer Singapore : Imprint: Springer, 2020
シリーズ名: Lecture Notes in Electrical Engineering ; 678
オンライン: https://doi.org/10.1007/978-981-15-6025-5
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An Adaptive Self-Assessment Model for Improving Student Performance in Language Learning using Massive Open Online Course (MOOC)
Assessment on Average Participation Versus Bialek’s Methods for Transmission Usage Evaluation Scheme
Field-effect Transistor-based Biosensor Optimization: Single Versus Array Silicon Nanowires Configuration
Top-Down Fabrication of Silicon Nanogap for Detection of Dengue Virus (DENV)
Design and Performance Analysis of IoT Based Sensor System using LoRa
A PSPT-MAC mechanism for congestion avoidance in Wireless Body Area Network
A Planar Slotted RCS based UWB RFID Tag on A PCB and A Flexible Substrate for Packaging Application
Design and Analysis of Automated Inspection System for Relays Fault Detection
Visible Light Communication-Based Indoor Notification System for Blind People
An Adaptive Self-Assessment Model for Improving Student Performance in Language Learning using Massive Open Online Course (MOOC)
Assessment on Average Participation Versus Bialek’s Methods for Transmission Usage Evaluation Scheme
Field-effect Transistor-based Biosensor Optimization: Single Versus Array Silicon Nanowires Configuration
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3. Database Systems for Advanced Applications : 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings, Part I. 1st ed. 2020
edited by Yunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Information Systems and Applications, incl. Internet/Web, and HCI ; 12112
オンライン: https://doi.org/10.1007/978-3-030-59410-7
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Advanced database and web applications.-Big data
Data mining
Machine learning for database
Data warehouse and OLAP
Information retrieval
Data model and query language
Query processing
Optimization
Recommendation systems
Data quality and credibility
Multimedia databases
Temporal and spatial databases
Data streams and time-series data
Semantic web and knowledge management
Graph data management
Social network analytics
Bio and health informatics
Blockchain and parallel/distributed systems
Security
Privacy and Trust
Databases for emerging hardware
Advanced database and web applications.-Big data
Data mining
Machine learning for database
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4. Deep Learning Architectures : A Mathematical Approach. 1st ed. 2020
by Ovidiu Calin
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Springer Series in the Data Sciences ;
オンライン: https://doi.org/10.1007/978-3-030-36721-3
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Introductory Problems
Activation Functions
Cost Functions
Finding Minima Algorithms
Abstract Neurons
Neural Networks
Approximation Theorems
Learning with One-dimensional Inputs
Universal Approximators
Exact Learning
Information Representation
Information Capacity Assessment
Output Manifolds
Neuromanifolds
Pooling
Convolutional Networks
Recurrent Neural Networks
Classification
Generative Models
Stochastic Networks
Hints and Solutions
Introductory Problems
Activation Functions
Cost Functions
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5. Domain Adaptation in Computer Vision with Deep Learning. 1st ed. 2020
edited by Hemanth Venkateswara, Sethuraman Panchanathan
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
オンライン: https://doi.org/10.1007/978-3-030-45529-3
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Preface
Part I: Introduction
Chapter 1: Introduction to Domain Adaptation
Chapter 2: Shallow Domain Adaptation
Part II: Domain Alignment in the Feature Space
Chapter 3: d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Chapter 4: Deep Hashing Network for Unsupervised Domain Adaptation
Chapter 5: Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation
Part III: Domain Alignment in the Image Space
Chapter 6: Unsupervised Domain Adaptation with Duplex Generative Adversarial Network
Chapter 7: Domain Adaptation via Image to Image Translation
Chapter 8: Domain Adaptation via Image Style Transfer
Part IV: Future Directions in Domain Adaptation
Chapter 9: Towards Scalable Image Classifier Learning with Noisy Labels via Domain Adaptation
Chapter 10: Adversarial Learning Approach for Open Set Domain Adaptation
Chapter 11: Universal Domain Adaptation
Chapter 12: Multi-source Domain Adaptation by Deep CockTail Networks
Chapter 13: Zero-Shot Task Transfer
Preface
Part I: Introduction
Chapter 1: Introduction to Domain Adaptation
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6. Cybersecurity in Digital Transformation : Scope and Applications. 1st ed. 2020
by Dietmar P.F. Möller
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: SpringerBriefs on Cyber Security Systems and Networks ;
オンライン: https://doi.org/10.1007/978-3-030-60570-4
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1.Cybersecurity in Digital Transformation
2.Introduction to Cybersecurity
3.Threat Intelligence
4.Intrusion Detection and Prevention
5.Machine Learning and Deep Learning
6.Attach Models and Scenarios
7.Cybersecurity Ontology
8.Cybersecurity Leadership
1.Cybersecurity in Digital Transformation
2.Introduction to Cybersecurity
3.Threat Intelligence
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7. Machine Learning Paradigms : Advances in Deep Learning-based Technological Applications. 1st ed. 2020
edited by George A. Tsihrintzis, Lakhmi C. Jain
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: Learning and Analytics in Intelligent Systems ; 18
オンライン: https://doi.org/10.1007/978-3-030-49724-8
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Chapter 1: Introduction to Deep Learning-based Technological Applications
Chapter 2: Vision to Language: Methods, Metrics and Datasets
Chapter 3: Deep Learning Techniques for Geospatial Data Analysis
Chapter 4: Deep Learning Approaches in Food Recognition
Chapter 5: Deep Learning for Twitter Sentiment Analysis: the Effect of pre-trained Word Embedding
Chapter 6: A Good Defense is a Strong DNN: Defending the IoT with Deep Neural Networks
Chapter 7: Survey on Deep Learning Techniques for Medical Imaging Application Area
Chapter 8: Deep Learning Methods in Electroencephalography
Chapter 1: Introduction to Deep Learning-based Technological Applications
Chapter 2: Vision to Language: Methods, Metrics and Datasets
Chapter 3: Deep Learning Techniques for Geospatial Data Analysis
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8. Machine Learning for Authorship Attribution and Cyber Forensics. 1st ed. 2020
by Farkhund Iqbal, Mourad Debbabi, Benjamin C. M. Fung
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2020
シリーズ名: International Series on Computer Entertainment and Media Technology ;
オンライン: https://doi.org/10.1007/978-3-030-61675-5
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1. Cybersecurity And Cybercrime Investigation
2. Machine Learning Framework For Messaging Forensics
3. Header-Level Investigation And Analyzing Network Information
4. Authorship Analysis Approaches
5. Authorship Analysis - Writeprint Mining For Authorship Attribution
6. Authorship Attribution With Few Training Samples
7. Authorship Characterization
8. Authorship Verification
9. Authorship Attribution Using Customized Associative Classification
10. Criminal Information Mining
11. Artificial Intelligence And Digital Forensics
1. Cybersecurity And Cybercrime Investigation
2. Machine Learning Framework For Messaging Forensics
3. Header-Level Investigation And Analyzing Network Information
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9. Deep Reinforcement Learning : Fundamentals, Research and Applications. 1st ed. 2020
edited by Hao Dong, Zihan Ding, Shanghang Zhang
出版情報: Singapore : Springer Singapore : Imprint: Springer, 2020
オンライン: https://doi.org/10.1007/978-981-15-4095-0
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Preface
Contributors
Acknowledgements
Mathematical Notation
Acronyms
Introduction
Part 1: Foundamentals
Chapter 1: Introduction to Deep Learning
Chapter 2: Introduction to Reinforcement Learning
Chapter 3: Taxonomy of Reinforcement Learning Algorithms
Chapter 4: Deep Q-Networks
Chapter 5: Policy Gradient
Chapter 6: Combine Deep Q-Networks with Actor-Critic
Part II: Research
Chapter 7: Challenges of Reinforcement Learning
Chapter 8: Imitation Learning
Chapter 9: Integrating Learning and Planning
Chapter 10: Hierarchical Reinforcement Learning
Chapter 11: Multi-Agent Reinforcement Learning
Chapter 12: Parallel Computing
Part III: Applications
Chapter 13: Learning to Run
Chapter 14: Robust Image Enhancement
Chapter 15: AlphaZero
Chapter 16: Robot Learning in Simulation
Chapter 17: Arena Platform for Multi-Agent Reinforcement Learning
Chapter 18: Tricks of Implementation
Part IV: Summary
Chapter 19: Algorithm Table
Chapter 20: Algorithm Cheatsheet
Preface
Contributors
Acknowledgements
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10. Bayesian Optimization with Application to Computer Experiments. 1st ed. 2021
by Tony Pourmohamad, Herbert K. H. Lee
出版情報: Cham : Springer International Publishing : Imprint: Springer, 2021
シリーズ名: SpringerBriefs in Statistics ;
オンライン: https://doi.org/10.1007/978-3-030-82458-7
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1. Computer experiments
2. Surrogate models
3. Unconstrained optimization
4. Constrained optimization
1. Computer experiments
2. Surrogate models
3. Unconstrained optimization