PPT-Similarity and Transformations
Author : luanne-stotts | Published Date : 2016-04-06
Ch 2 Lesson 3 Pg 123 What will you will learn Enlarge Photographs Make something from a pattern Identify Similarity Two figures are similar if the second can be
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Similarity and Transformations: Transcript
Ch 2 Lesson 3 Pg 123 What will you will learn Enlarge Photographs Make something from a pattern Identify Similarity Two figures are similar if the second can be obtained from the first by a sequence of transformations and dilations. Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining . their . similarities (or dissimilarities). energies. D.A. . Artemenkov. , G.I. . . Lykasov. , . A.I. . . Malakhov. Joint Institute for Nuclear Research. malakhov@lhe.jinr.ru. Hadron Structure 2015, June 29 – July 3, 2015, . Horn. ý. . . Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. Lecture 3. Jitendra. Malik. Pose and Shape. Rotations and reflections are examples. of orthogonal transformations . Rigid body motions. (Euclidean transformations / . isometries. ). Theorem:. Any rigid body motion can be expressed as an orthogonal transformation followed by a translation.. Warm Up. 1.. . If . ∆. QRS. . ∆. ZYX. , identify the pairs of congruent angles and the pairs of congruent sides.. Solve each proportion.. 2.. . 3.. . . x . = 9. x . = 18. . Q. Maurice J. . Chacron. and Kathleen E. Cullen. Outline. Lecture 1: . - Introduction to sensorimotor . . transformations. - . The case of “linear” sensorimotor . transformations: . Word . Similarity: . Distributional Similarity (I). Problems with thesaurus-based . meaning. We don’t have a thesaurus for every language. Even if we do, . they have problems with . recall. M. any . Learning Targets: 8.G.2,8.G.3, 8.G.4. Follow the slides to learn more about transformations. Students should have paper and a pencil for notes at their desk while going through this presentation.. Transformation: a transformation is a change in position, shape or size.. Graph: . . What is the parent function for this graph?. What does the parent function look like?. Shape is a V. Vertex is (0, 0). Slope is 1, opens up. How is the graph above different from the parent function?. Bamshad Mobasher. DePaul University. Distance or Similarity Measures. Many data mining and analytics tasks involve the comparison of objects and determining in terms of their similarities (or dissimilarities). . 16-385 Computer Vision. Spring 2019, . Lecture 7. http://www.cs.cmu.edu/~16385/. Course announcements. Homework 2 is posted on the course website.. - It is due on February 27. th. at 23:59 pm.. - Start early because it is much larger and more difficult than homework 1.. Quiz. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 3. Deer-mouse. 4. Deer-roof. Quiz Answer. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. CSE 455. Ali Farhadi. Many slides from Steve Seitz and Larry . Zitnick. What are geometric transformations?. Translation. Preserves: Orientation. Translation and rotation. Scale. Similarity transformations. Sketching, Locality Sensitive Hashing. SIMILARITY AND DISTANCE. Thanks to:. Tan, Steinbach, . and Kumar, “Introduction to Data Mining”. Rajaraman. . and . Ullman, “Mining Massive Datasets”. Similarity and Distance.
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