Jure Leskovec, Stanford University - SNAP - Stanford University

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Would like to do prediction: estimate a function f(x) so that y = f(x) Where y can be: Real number: Regression Categorical: Classification Data is labeled: have many pairs {(x, y)} x … vector of real valued features y … class ({+1, -1}, or a real number) Methods: k-Nearest Neighbor Support Vector Machines Decision trees 3/9/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 26
Distance metric: Euclidean How many neighbors to look at? All of them (!) Weighting function: wi = exp(-d(xi, q) 2 d(xi, q) = 0 /Kw) Nearby points to query q are weighted more strongly. Kw…kernel width. How to fit with the local points? Predict weighted average: Σwiyi/Σwi K=10 K=20 K=80 3/9/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 27 w i
Page 1 and 2: CS246: Mining Massive Datasets Jure
Page 3 and 4: MapReduce Association Rules Findi
Page 5 and 6: The crew of the space shuttle Endea
Page 7 and 8: Many problems can be expressed as f
Page 9 and 10: Shingling: convert docs to sets of
Page 11 and 12: ands Hash cols of signature matrix
Page 13 and 14: SciFi Romnce A = U Σ V T - exampl
Page 15 and 16: SciFi Romnce A = U Σ V T - exampl
Page 17 and 18: Hierarchical: Agglomerative (botto
Page 19 and 20: LSH: Find somewhat similar pairs o
Page 21 and 22: Rank nodes using link structure Pa
Page 23 and 24: HITS (Hypertext-Induced Topic Selec
Page 25: High-dimensional data: Locality Sen
Page 29 and 30: Building decision trees using MapRe
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Page 33 and 34: Can’t store the whole stream but
Page 35 and 36: You get to see one input piece at a
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Page 39 and 40: Observation: Subsets of a frequent
Page 41 and 42: User-user collaborative filtering
Page 43 and 44: MapReduce Association Rules Aprio
Page 45 and 46: Seminars: InfoSeminar: http://i.st

Jure Leskovec, Stanford University - SNAP - Stanford University

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