PPT-Chapter 16 Species and Similarity:
Author : celsa-spraggs | Published Date : 2018-09-17
On Being the Same Yet Different Overview Reality of species Defining species Analogy vs homology and parallelism vs convergence How are species related Classification
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Chapter 16 Species and Similarity:: Transcript
On Being the Same Yet Different Overview Reality of species Defining species Analogy vs homology and parallelism vs convergence How are species related Classification based on phylogeny 3 Key Questions. Presented by:. Akshay. Kumar. Pankaj. . Prateek. Are these similar?. Number ‘1’ vs. color ‘red’. Number ‘1’ vs. ‘small’. Horse vs. Rider. True vs. false . ‘. Monalisa. ’ vs. ‘Virgin of the rocks’. 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. . . . Multi-label Protein Subcellular Localization. Shibiao WAN and Man-Wai MAK. The Hong Kong Polytechnic University. Sun-Yuan KUNG. Princeton University. Outline. Introduction and Motivation. Retrieval of GO Terms. -. based Clustering. Mohammad. . Rezaei. , Pasi Fränti. rezaei@cs.uef.fi. Speech. and . Image. . Processing. . Unit. University of Eastern Finland. . August 2014. Keyword-Based Clustering. An object such as a text document, website, movie and service can be described by a set of keywords. Thomas Berg and Peter N. . Belhumeur. Columbia University. Outline. Introduction. Visual . Similarity. A Visual Field Guide to . Birds. Conclusions. Introduction. “Can a . recognition system show humans what to look for . CMPS 561-FALL 2014. SUMI SINGH. SXS5729. Protein Structure . 2. RPDFCLEPPYAGACRARIIRYFYNAKAGLCQ. Primary Structure. Sequence of Amino Acids. . Not . enough for functional prediction.. Tertiary Structure. 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 . Jing . Zhang, . Jie. . Tang . , Cong . Ma . , . Hanghang. . Tong . , Yu . Jing . , and . Juanzi. . Li. Presented by Moumita Chanda Das . Outline. Introduction. Problem formulation. Panther using path sampling. CSE, HKUST. March 20. Recap. String declaration. str1=“Hong”. str2=“Kong”. String Operators. strr. =str1+str2. “H” in . strr. String Slicing. strr. [. i. ]. strr. [:. i. ]. strr. [. i. :]. CS159 . Fall . 2014. Admin. Assignment 4. Quiz #2 Thursday. Same . rules as quiz #1. First 30 minutes of class. Open book and . notes. Assignment 5 out on Thursday. Quiz #2. Topics. Linguistics 101. Parsing. Presenter. : Monica Farkash. Bryan Hickerson. . mfarkash@us.ibm.com. . bhickers@us.ibm.com. . 2. Outline. The challenge: Providing a subset from a regression test suite. Our new Jaccard/K-means (JK) approach . 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. Jessica Chackoria. 1. , Brooke Nyberg. 1. , Melissa Vazquez. 1 . Suzanne Bell. 2. , . Alla. Vinokhodova. 3. , Vadim Gushin. 3. , Leslie DeChurch. 4 . , Noshir Contractor. 4 . 1. DePaul University, .
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