Machine Learning * Machine Learning: Lecture 3 Decision Tree Learning (Based on Chapter 3 of Mitchell T.., Machine Learning, 1997) thanks to Brian Pardo (http://bryanpardo.com) for the illustrations on slides 9, 18 * * Decision Tree Representation Outlook Humidity Wind Sunny Overcast Rain High Normal Strong Weak A Decision Tree for the concept PlayTennis * […]
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Program Analysis Asymptotic Analysis of Algorithms David Weir (U of Sussex) Program Analysis Term 1, 2017 31 / 606 Analysing Algorithm Efficiency Things we might want to know: How efficient is a given algorithm? What sized problems can be solved within a reasonable time? Is some new algorithm really more efficient that the existing one?
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Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright c© 2018. All rights reserved. Draft of September 23, 2018. CHAPTER 12 Statistical Parsing The characters in Damon Runyon’s short stories are willing to bet “on any propo- sition whatever”, as Runyon says about Sky Masterson in The Idyll of Miss Sarah Brown, from
Data Reshaping Data Reshaping Faculty of Information Technology, Monash University, Australia FIT5196 week 09 (Monash) FIT5196 1 / 43 Outline 1 Data Transformation Data Normalisation/Scaling Transformation by generating new features Nominal to Numeric Transformation 2 Data Discretisation 3 Feature Engineering & Data Sampling 4 Summary (Monash) FIT5196 2 / 43 Data Wrangling Process (Monash) FIT5196
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7class-a Data Mining: Concepts and Techniques 1 COMP9318: Data Warehousing and Data Mining — L7: Classification and Prediction — n Problem definition and preliminaries Data Mining: Concepts and Techniques 2 Data Mining: Concepts and Techniques 3 n Classification: n predicts categorical class labels (discrete or nominal) n classifies data (constructs a model) based on the
Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright c© 2017. All rights reserved. Draft of August 28, 2017. CHAPTER 13 Statistical Parsing The characters in Damon Runyon’s short stories are willing to bet “on any propo- sition whatever”, as Runyon says about Sky Masterson in The Idyll of Miss Sarah Brown, from
Machine Learning Evaluation: A Classification Perspective CSC 589: Performance Evaluation for Learning Algorithms Nathalie Japkowicz Motivation: My story A student and I designed a new algorithm for data that had been provided to us by the National Institute of Health (NIH). According to the standard evaluation practices in machine learning, we found our results to
Introduction to Algorithms, Third Edition A L G O R I T H M S I N T R O D U C T I O N T O T H I R D E D I T I O N T H O M A S H. C H A R L E S
Introduction to Information Retrieval Online edition (c) 2009 Cambridge UP DRAFT! © April 1, 2009 Cambridge University Press. Feedback welcome. 253 13 Text classification and NaiveBayes Thus far, this book has mainly discussed the process of ad hoc retrieval, where users have transient information needs that they try to address by posing one or more
Slide 1 * Overview of Machine Learning: Algorithms and Applications Nathalie Japkowicz School of Information Technology and Engineering University of Ottawa * Overview I Machine Learning and Data Mining include a large number of are general-purpose methods that have been applied with great success to a many domains, including mechanical diagnosis, satellite image screening, credit-card
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