1 Introduction Problem Definition Existing Methods 2 Isotropic Hashing Model Learning Experiment 3 Multiple-Bit Quantization Double-Bit Quantization Manhattan Quantization 4 Conclusion 5 Reference

1 Introduction Problem Definition Existing Methods 2 Scalable Graph Hashing with Feature Transformation Motivation Model and Learning Experiment 3 Conclusion 4 Reference

《人工智能、机器学习与大数据》课程教学资源（参考文献）Discrete Latent Factor Model for Cross-Modal Hashing

 3.1 Learning Problems  3.2 The least square method (LSM)  3.3 Linear regression analysis  1 Simple Linear Regression  2 Multiple Regression  3 Understanding the Regression Output  4 Coefficient of Determination R2  5 Validating the Regression Model

• MSC.EASY5 Overview • Getting Started • Help and Documentation • Model Building & Simulation • Operating Point and Steady State Analysis • Linear Analyses • Data Tables and the Matrix Editor • Analysis Tools & Methodology • Simulation and Integration • Interactive Simulation • Miscellaneous Topics • MSC.EASY5 Architecture • Writing Code in MSC.EASY5 • Summary

《网络与系统安全》教学参考文献：A Novel Hybrid Model for Task Dependent Scheduling in Container-based Edge Computing

计算机科学与技术（参考文献）AirContour - Building Contour-based Model for in-Air Writing Gesture Recognition

电子科技大学：《大数据时代商业模式创新 Business model innovation》研究生课程教学资源（讲座）换一个视角看清商业本质

Structure of Relational Databases Fundamental Relational-Algebra-Operations Additional Relational-Algebra-Operations Extended Relational-Algebra-Operations Null Values Modification of the Database

《数据库系统概念 Database System Concepts》原书教学资源（第六版，附录，英文版）Network Model