Logistic Regression and MaxEnt Wei Wang @ CSE, UNSW April 9, 2020 1/23 Wei Wang @ CSE, UNSW Logistic Regression and MaxEnt Generative vs. Discriminative Learning Generative models: Pr[y | x] = Pr[x | y]Pr[y] Pr[x] ∝ Pr[x | y]Pr[y] = Pr[x, y] The key is to model the generative probability: Pr[x | y]. Example: […]
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COMP9318: Data Warehousing and Data Mining — L7: Classification and Prediction — Data Mining: Concepts and Techniques 1 n Problem definition and preliminaries Data Mining: Concepts and Techniques 2 ML Map Data Mining: Concepts and Techniques 3 Classification vs. Prediction n Classification: n predicts categorical class labels (discrete or nominal) n classifies data (constructs a
CS 189 Introduction to Machine Learning Spring 2018 EXAMFINAL After the exam starts, please write your student ID (or name) on every page. There are 6 questions for a total of 38 parts. Two parts are bonus and can give extra credit points. On the last question (multiple choice), you will be graded on your
Data Mining (EECS 4412) Decision Tree Learning Parke Godfrey EECS Lassonde School of Engineering York University Thanks to Professor Aijun An for curation & use of these slides. 2 Outline Overview of classification Basic concepts in decision tree learning Data representation in decision tree learning What is a decision tree? Decision tree representation How to
CS计算机代考程序代写 Bayesian data mining algorithm decision tree Excel Data Mining (EECS 4412) Read More »
Data Mining (EECS 4412) Decision Rule Learning Parke Godfrey EECS Lassonde School of Engineering York University Thanks to Professor Aijun An for curation & use of these slides. 2 Outline What are decision rules? How to learn decision rules? Sequential covering algorithm Classification with rules 3 What Are Decision Rules? If-then rules that can be
CS计算机代考程序代写 data mining algorithm decision tree flex Data Mining (EECS 4412) Read More »
Data Mining (EECS 4412) Data Preprocessing Parke Godfrey EECS Lassonde School of Engineering York University Thanks to Professor Aijun An for curation & use of these slides. 2 Process of Data Mining and KDD Pattern Evaluation and Presentation Pattern Extraction Data Preprocessing Data training data target data Goals of Prior Application Knowledge Feedback Data Reduction
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PowerPoint Presentation EECS 4101/5101 Search Trees Prof. Andy Mirzaian Lists Move-to-Front Search Trees Binary Search Trees Multi-Way Search Trees B-trees Splay Trees 2-3-4 Trees Red-Black Trees SELF ADJUSTING WORST-CASE EFFICIENT competitive competitive? Linear Lists Multi-Lists Hash Tables DICTIONARIES 2 TOPICS Binary Trees Binary Search Trees Multi-way Search Trees 3 References: [CLRS] chapter 12 4 Binary
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Data Mining (EECS 4412) K Nearest Neighbour Classifier Parke Godfrey EECS Lassonde School of Engineering York University Thanks to Aijun for creation & use Professor An of these slides. 2 K Nearest Neighbor Classifiers – Learning by analogy: Tell me who your friends you who you are are and I’ll tell A common class among
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Data Mining Third Edition The Morgan Kaufmann Series in Data Management Systems (Selected Titles) Joe Celko’s Data, Measurements, and Standards in SQL Joe Celko Information Modeling and Relational Databases, 2nd Edition Terry Halpin, Tony Morgan Joe Celko’s Thinking in Sets Joe Celko Business Metadata Bill Inmon, Bonnie O’Neil, Lowell Fryman Unleashing Web 2.0 Gottfried Vossen,
COMP9318: Data Warehousing and Data Mining — L7: Classification and Prediction — Data Mining: Concepts and Techniques 1 n Problem definition and preliminaries Data Mining: Concepts and Techniques 2 ML Map Data Mining: Concepts and Techniques 3 Classification vs. Prediction n Classification: n predicts categorical class labels (discrete or nominal) n classifies data (constructs a