Due: 3/25 Note: Show all your work. Assignment 7 Problem 1 (20 points). For this problem, you will run bagging and boosting algorithms that are implemented on Weka on the processed.hungarian-2.arff dataset. Run the following six classifier algorithms on the processed.hungarian-2.arff dataset (1) each classifier alone, (2) Bagging with the classifier, and (3) AdaBoostM1 with […]
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CS699 Lecture 4 Classification 1 Supervised vs. Unsupervised Learning Supervised learning (classification) Supervision: The training data (observations, measurements, etc.) are accompanied by class labels indicating the class of the observations New data is classified based on the training set Unsupervised learning (clustering) The class labels of training data is unknown
Assignment 5 This assignment is not graded and you don¡¯t need to submit. Still, you may want to study it to prepare for the midterm exam. Problem. Run J48 on buys_computer_extended_tr.csv (note: accept all default parameters). (1). Visualize the decision tree. (2). Extract all rules from the decision tree. (3). For each rule, calculate the
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CS699 Lecture 5 Classification 2 • Using IF‐THEN Rules for Classification Represent the knowledge in the form of IF‐THEN rules • Assessment of a rule: coverage and accuracy – A tuple is covered by R if it satisfies the antecedent of R – ncovers = # of tuples covered by R – ncorrect = #
CS699 Lecture 1 Introduction • Our focus is “data mining” not “data warehousing.” • Will discuss – Data preprocessing CS699 • Data mining is an important component of data analysis. – Basic data mining algorithms – How to evaluate data mining models and data mining results – How to perform data mining using software tools
CS699 Lecture 6 Performance Evaluation Model Evaluation and Selection Evaluation metrics: How can we measure accuracy? Other metrics to consider? Use an independent test dataset instead of training dataset when assessing accuracy Methods for estimating a classifier’s accuracy: Holdout method, random subsampling Cross‐validation Bootstrap Comparing classifiers: Confidence
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3/25/2021 CSE 473/4573 Introduction to Computer Vision and Image Processing ‘- CLASSIFICATION AND RECOGNITION Slide Credit: Hays, et al. ‘- 1 3/25/2021 Local-feature Alignment ‘- 3 Recall: Hypothesize and test • Given model of object • New image: hypothesize object identity and pose • Render object in camera • Compare rendering to actual image: if
INF 553: Foundations and Applications of Data Mining (Summer 2020) Yao-Yi Chiang Associate Professor (Research), Spatial Sciences Institute Associate Director, Integrated Media Systems Center Spatial Computing Lab University of Southern California Thanks for source slides and material to: J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets http://www.mmds.org What is Data Mining? About THIS
COMP 9517 WK5 2021 T1 Introduction • Pattern recognition: is the scientific discipline whose goal is to automatically recognise patterns and regularities in the data (e.g. images)> • Examples: • Object recognition / object classification • Text classification (e.g. spam / non-spam emails) • Speech recognition • Event detection • Recommender systems Pattern recognition categories
CS计算机代考程序代写 algorithm decision tree Excel Bayesian COMP 9517 WK5 Read More »
COMP9517: Computer Vision Pattern Recognition Part 1 Week 4 COMP9517 2021 T1 1 Introduction • Pattern recognition: is the scientific discipline whose goal is to automatically recognise patterns and regularities in the data (e.g. images). • Examples: • object recognition / object classification • Text classification (e.g. spam/non-spam emails) • Speech recognition • Event detection