• Objectives of AdaBoost • Solve 2-class classification problems • Will discuss – Training – Detection – Examples

• Data Mining – From data warehousing to data mining. – Data pre-processing and data mining life-cycle. – Association and sequence analysis; classification and clustering. – Fuzzy Logic, Neural Networks, and Genetic Algorithms. – Mining Complex Data. • OLAP mining; spatial data mining; text mining; time-series data mining; web mining; visual data mining. • Data warehousing. – Introduction; basic concepts of data warehousing; data warehouse vs. Operational DB; data warehouse and the industry. – Architecture and design; two-tier and three￾tier architecture; star schema and snowflake schema; data capturing, replication, transformation and cleansing. – Data characteristics; metadata; static and dynamic data; derived data. – Data Marts; OLAP; data mining; data warehouse administration

1. Review of Lab 2 2. User defined function 3. Execution control 4. Plots and graphs using Matlab 5. Basic manipulation in image processing 6. Color image compression

香港科技大学：Information-Agnostic Flow Scheduling for Commodity Data Centers

◆ Background of Mobile Ad Hoc Networks ◆ Thesis part I ◼ A Trusted Routing Protocol for Security Issues of Mobile Ad Hoc Networks ◆ Thesis part II ◼ A Coalitional Game Model for Security Issues of Wireless Networks ◆ Thesis part III ◼ A Coalitional Game Model for Selfishness Issues of Wireless Networks

1. Review our theoretical results 2. A software for motif detection 3. Other problems we have been working --Parametric tools RNA secondary structure comparison --Computing translocation distance between genomes

• Section 1: The need for a protocol architecture • Section 2: TCP/IP protocol architecture • Section 3: OSI model Learning outcomes: Describe the network protocol architectures and reference models

香港理工大学：Discovering Classification Rules

香港科技大学：基于文档的知识管理（PPT课件讲稿）Knowledge Management with Documents

香港大學教育學院：中學會考地理科專科語體分析