在基於案例推理case-basedreasoning簡稱cbr-智能科學.ppt

Training Training 跨领域舆情分类Cross-Domain Sentiment Classification Classifier Test DVD Classifier Test Electronics 82.55% 71.85% DVD DVD 分布不同 * Device, Space, or Time Night time Day time Device 1 Device 2 Wireless sensor networks Different time periods, devices or space 特征不同 Heterogeneous: different feature spaces * The apple is the pomaceous fruit of the apple tree, species Malus domestica in the rose family Rosaceae ... Banana is the common name for a type of fruit and also the herbaceous plants of the genus Musa which produce this commonly eaten fruit ... Training: Text Future: Images Apples Bananas 迁移学习: 源领域 Learning Input Output Source Domains * Source Domain Target Domain Training Data Labeled/Unlabeled Labeled/Unlabeled Test Data Unlabeled Network Data Social Networks Facebook, Twitter, Live messenger, etc. Information Networks The Web (1.0 with hyperlinks and 2.0 with tags) Citation network, Wikipedia, etc. Biological Networks Gene network, etc. Other Networks * Real World Study Who talks to whom on MSN messenger Network: 240M nodes, 1.3 billion edges Conclusions: Average path length is 6.6 90% of nodes is reachable 8 steps Leskovec-Horvitz WWW ‘08 * Network Structures: Links Global network structures Small world Long tail distributions Local network structures Relational learning (relation==link) Link prediction (e.g. recommendation) Group (Cluster) structures * Global Network Structures Small world Six degrees of separation [Milgram 60s] Clustering and communities Evolutionary patterns Long tail distributions Personalized recommendation: Amazon Also brings a challenge in recommendation and relational learning: sparseness * Local Network Structures Link Prediction A form of Statistical Relational Learning Object classification: predict category of an object based on its attributes and links Is this person a student? Link classification: predict type of a link Are they co-authors? Link existence: predict whether a link ex