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 […]
Last week recap •Greedy Algorithms: Ø Dijkstra’s algorithm (single-source shortest paths) •Dynamic Programming: Ø A DAG shortest path algorithm Ø Weighted interval scheduling o Bottom-up algorithm o Top-down algorithm o Memoization 2021-02-01 CSC373 Winter 2021 – Sam Toueg 1 Dynamic Programing Edit Distance 2021-02-01 CSC373 Winter 2021 – Sam Toueg 2 Edit Distance • How
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CSCA48 – Unit 5 – Graphs and Recursion Winter 2021 Learning Outcomes This unit expands on the materials we learned in previous units on linked lists and trees in order to discuss graphs and other generalized approaches to data structures. We will also cover recursion (which we have been implicitly using in previous units) and
DEPARTMENT OF MATHEMATICAL AND COMPUTATIONAL SCIENCES UNIVERSITY OF TORONTO MISSISSAUGA Class Location & Time Instructor Office Location Office Hours E-mail Address Course Web Site Teaching Assistant Course Description CSC338H5S LEC0101 Numerical Methods Course Outline – Winter 2020 Wed, 03:00 PM – 05:00 PM IB 235 Lisa Zhang DH3078 lczhang [at] cs [dot] toronto [dot] edu
2020 年 1 月 安徽大学学报(自然科学版) January 2020 第 44 卷第 1 期 Journal of Anhui University (Natural Science Edition) Vol .44 No .1 doi :10 .3969/j .issn .1000-2162 .2020 .01 .007 基于 TCGA 数据库不平衡数据的改进分类方法 侯维岩1 ，刘 超1 ，宋 杨2 ，孙 燚1 (1 .郑州大学 信息工程学院 ，河南 郑州 450001 ;2 .上海大学 机械自动化学院 ，上海 200072) 摘 要 :为解决癌症基因组图谱中
Traditional Methods for Machine Learning in Graphs Graph Properties how many friends do I have? Copyright By PowCoder代写 加微信 powcoder ◾ Weights: how strong are the ties? how far am I from another vertex? ◾ Connectivity: can I reach all other vertices? ◾ Diameter: how dense are they? ◾ Centrality (e.g., betweenness, closeness): Am I
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Outline Large margin classifiers Primal/Dual formulation of the SVM Kernel SVMs Hard-margin / soft-margin SVM and Hinge Loss SVMs beyond classification Applications 1/25 Linear Classifiers with Margin Let φ(x) be some feature map mapping the data in Rd to some feature space RD. We consider functions
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Lecture 6: Dynamic Programming I The University of Sydney Page 1 Fast Fourier Transform General techniques in this course – Greedy algorithms [Lecture 3] – Divide & Conquer algorithms [Lectures 4 and 5] – Dynamic programming algorithms [today and 11 Apr] – Network flow algorithms [18 Apr and 2 May] The University of Sydney Page
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Lecture 4: Dynamic Programming I William Umboh School of Computer Science The University of Sydney Page 1 Fast Fourier Transform Moving completely online – Lectures – Held on Zoom and recorded – Use Mentimeter for anonymous questions – Participants muted on entry. Press the “Raise Hands” button to ask a question and unmute yourself once
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