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집합모델 확장불린모델

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JUNGEUN KANG
JUNGEUN KANG

정보검색시스템 강의노트 강승식교수님

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2.6 Alternative Set Theoretic Models ,[object Object],[object Object]
2.6.1 Fuzzy Set Model ,[object Object],[object Object],[object Object],tall very tall 1 0 height 1 0 height Fuzzy Membership Conventional Membership
Fuzzy Set Model (Cont.) ,[object Object],[object Object]
Fuzzy Set Model (Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object]
2.6.2 Extended Boolean Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extended Boolean Model (Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Extended Boolean Model (Cont.) ,[object Object],k x and k y d j d j+1 k y k x (1,0) (0,1) (0,0) (1,1) ` d j d j+1 k x or k y (1,0) (0,0) (1,1) (0,1) k y k x
Extended Boolean Model (Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extended Boolean Model (Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Extended Boolean Model (Cont.) ,[object Object],[object Object]
2.7 Alternative Algebraic Models ,[object Object],[object Object],[object Object]
2.7.1 Generalized Vector Space Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
2.7.2 Latent Semantic Indexing Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
2.7.3 Neural Network Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Neural Network Model (Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Neural Network Model (Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
2.8 Alternative Probabilistic Models ,[object Object],[object Object],[object Object]
2.8.1 Bayesian Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],x 1 x 2 x 3 x 4 x 5
2.8.2 Inference Network Model ,[object Object],[object Object]
Inference Network Model(Cont.) ,[object Object],A B C D E X Y F
Inference Network Model(Cont.) ,[object Object],[object Object],0.95 0.8 0.2 0.1 Y false 0.05 0.2 0.8 0.9 Y true P(D)=0.8 P(E)=0.4
Inference Network Model(Cont.) ,[object Object],[object Object],[object Object],t 1 t 3 t 4 t 2 D1 D2 D3 Q t 2 t 3 d 1 d 2 d 3 t 1 t 2 t 3 t 4 Q Document Layer Concept Layer Query Layer
Inference Network Model(Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inference Network Model(Cont.) ,[object Object],Q : “gold silver truck” D 1 : “Shipment of gold damaged in a fire.” D 2 : “Delivery of silver arrived in a silver truck.” D 3 : “Shipment of gold arrived in a truck.” 1 1 0 1 1 1 0 0 0 1 1 D3 0.5 0 1 0.5 0.5 0 0 0.5 0 0.5 0.5 D2 0 1 0 1 1 1 1 0 1 0 1 D1 0.37 0.37 0.37 0 0 0.37 1 1 1 0.37 0 nidf 0.41 0.41 0.41 0 0 0.41 1.10 1.10 1.10 0.41 0 idf t 11 t 10 t 9 t 8 t 7 t 6 t 5 t 4 t 3 t 2 t 1
Inference Network Model(Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0.685 0.685 0 True 0.315 0.315 1 False D1 D3 D1 D3 D1 D3 gold 0.685 True 0.315 False D2 silver 0.592 0.685 0 True 0.408 0.315 1 False D2 D3 D2 D3 D2 D3 truck
Inference Network Model(Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0.5 0.5 t 0.7 0.3 gt 0.5 0.5 gs 0.7 0.3 st 0.1 0.9 gst 0.9 0.3 0.3 True 0.1 0.7 0.7 False gst s g

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