'Decision tree model' presentation slideshows

Perceptron Learning

883 views • 71 slides

CSE 881: Data Mining

623 views • 36 slides

Sorting Conclusions

Sorting Conclusions. David Kauchak cs302 Spring 2013. Administrative. Find a partner to bounce ideas off of Assignment 2 Proofs should be very concrete Proving big-O, must satisfy definition

330 views • 31 slides

Lower Bound on Sorting

Lower Bound on Sorting. David Kauchak cs062 Spring 2010. Decision-tree model. B inary tree representing the comparisons between elements by a sorting algorithm Internal nodes contain indices to be compared Leaves contain a complete permutation of the input

355 views • 25 slides

Trees

Trees. Trees. Set of elements or nodes one node as root every other node has a single predecessor (parent) and zero or more successors (children) nodes without successors are called leaves Nodes are connected by edges The depth of a node is the number of edges on the path to the root

205 views • 20 slides

Statistical Decision-Tree Models for Parsing

Statistical Decision-Tree Models for Parsing. NLP lab, POSTECH ? ? ?. Contents. Abstract Introduction Decision-Tree Modeling SPATTER Parsing Statistical Parsing Models Decision-Tree Growing & Smoothing Decision-Tree Training Experiment Results Conclusion. Abstract.

394 views • 18 slides

Lower bound: Decision tree and adversary argument

Lower bound: Decision tree and adversary argument. Lower bound: ?( f ( n )) in the worst case. Decision tree model: Example: for comparison sorting. Adversary argument: Example: find the maximum . Comparison sort. Comparison sort: Insertion sort, O ( n 2 ), upper bound in worst case

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Turorial#3

Turorial#3. classification. A Tree Classification algorithm is used to compute a decision tree. Decision trees are easy to understand and modify, and the model developed can be expressed as a set of decision rules. classification.

251 views • 14 slides