Jure Leskovec, Stanford University - SNAP - Stanford University

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Prediction = sign(w⋅x + b) Model parameters w, b w 1 Margin: γ = = w⋅ w w SVM optimization problem: min w, b, ξ ≥0 s. t. ∀i, i 1 2 y i w ( w⋅ n + C∑ i= 1 Find w,b using Stochastic gradient descent 2 x i ξ + b) ≥1 −ξ i + + 3/9/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 28 i + + + + + - ξ i ξ i - - - + -
Building decision trees using MapReduce How to predict? Predictor: avg. y i of the examples in the leaf When to stop? # of examples in the leaf is small How to build? One MapReduce job per level Need to compute split quality for each attribute and each split value for each current leaf 3/9/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 29 |D|=10 A |D|=90 .42 B X1
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 and 26: High-dimensional data: Locality Sen
Page 27: Distance metric: Euclidean How ma
Page 33 and 34: Can’t store the whole stream but
Page 35 and 36: You get to see one input piece at a
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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