'Normalization' diaporamas de présentation

What to recall about motion in a central potential

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Spatial and Temporal Data Mining

Spatial and Temporal Data Mining. Data Preprocessing. (based on notes by Jiawei Han and Micheline Kamber ). Agenda. Why data preprocessing? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary.

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ITS232 Introduction To Database Management Systems

ITS232 Introduction To Database Management Systems . Siti Nurbaya Ismail Faculty of Computer Science & Mathematics, Universiti Teknologi MARA ( UiTM ), Kedah | sitinurbaya@kedah.uitm.edu.my | http://www.sitinur151.wordpress.com | | A2-3039 | ext:2561 | 012-7760562 |. CHAPTER 5

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Type Generativity and Sharing

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HOW MANY WAYS CAN YOU HURT ME? CHILD SEXUAL VICTIMIZATION

HOW MANY WAYS CAN YOU HURT ME? CHILD SEXUAL VICTIMIZATION. & THE NORMALIZATION OF SEXUAL HARM SHARON W. COOPER, MD FAAP DEVELOPMENTAL & FORENSIC PEDIATRICS, P.A. DEPARTMENT OF PEDIATRICS UNIVERSITY OF NORTH CAROLINA CHAPEL HILL SCHOOL OF MEDICINE. CHILD SEXUAL EXPLOITATION.

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Normalization

Normalization. Stanislava Armstrong http://www.cs.nott.ac.uk/~saw/teaching/G64DBS/lecture_slides.html. Last Lecture. Determine the outcome from executing each of the following statements on the Holidays table below:. SELECT Destination, COUNT(*) FROM Holiday WHERE ISNULL(Price) = TRUE

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Basis beeldverwerking (8D040) dr. Andrea Fuster Prof.dr.ir . Marcel Breeuwer dr. Anna Vilanova

Basis beeldverwerking (8D040) dr. Andrea Fuster Prof.dr.ir . Marcel Breeuwer dr. Anna Vilanova. Histogram equalization. Today. Definition of histogram Examples Histogram equalization: Continuous case Discrete case Examples Histogram features. Histogram definition.

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Enhanced Local Texture Feature Sets for Face Recognition Under Dif?cult Lighting Conditions

Enhanced Local Texture Feature Sets for Face Recognition Under Dif?cult Lighting Conditions. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010 Xiaoyang Tan and Bill Triggs ???????. Introduction.

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Second Normal Form (2NF)

Second Normal Form (2NF) . A relation R is in 1NF, and every non-primary-key attribute is fully functionally dependent on the primary key Then R is in 2NF No Partial FDs on the PK. Third Normal Form (3NF). Relation R in 2NF, and

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Chapter 15

Chapter 15. Normalization for Relational Databases. Lecture # 15 July 24 ,2012. General Definitions of Second and Third Normal Forms. Boyce-Codd Normal Form. Every relation in BCNF is also in 3NF Relation in 3NF is not necessarily in BCNF Difference:

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Spectroscopic Data

Spectroscopic Data. ASTR 3010 Lecture 15 Textbook Ch.11. Spectroscopic Measurement. CCD or other digital Detector. Schematic Diagram of a Typical Spectroscopic Data. Only Readout the relevant portion of CCD array. Spectroscopic data are typically long and slender.

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Microarray Gene Expression Data Analysis

Microarray Gene Expression Data Analysis. Functional Genomics. Chapter: 07. A.Venkatesh CBBL. Contents. Introduction. Microarray technology provides a powerful tool that enables researchers to observe simultaneously mRNA expression levels of thousands of genes .

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Continuum of Services

Continuum of Services. Foundations of Inclusion. Why Inclusion? Human potential movement General systems theory Principle of Normalization Self-determination movement. Continuum. Full Inclusion model Collaborative/supportive model Social mainstreaming Home class model

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Normal Forms in Relational Databases 1

Normal Forms in Relational Databases 1. 1 Bolstad, P. pp 283-291. Problem. Massive tables with information about multiple entities become unwieldy making data update and maintenance difficult. They suffer from performance issues, consistency, and redundancy. Problem.

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Normalization Normalization intro First normal form (1NF) Second normal form ( 2 NF)

Normalization Normalization intro First normal form (1NF) Second normal form ( 2 NF) Third normal form (3NF) Denormalization Beyond normalization Can be skipped in chapter 8 Steen Jensen, autumn 2013. Normalization - introduction.

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Database Fundamentals

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Classroom Exercise: Normalization

Classroom Exercise: Normalization. Consider the relation R(A,B,C,D) with these given FDs: AB -> C C -> D D -> A Compute all nontrivial FDs that follow from these. Compute the key(s) for R. What are all the superkeys? What are all the BCNF violations? Decompose R into BCNF.

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Classifier training

Supplementary Figure 1. DIGE analysis. Cluster analysis. ELISA assays. Prediction analysis (LDA). 1. 3. 2. Predictor discovery in training set. Predictor test in testing set. DIGE raw gel images Matched samples SJIA (10 F, 10 Q) POLY (5 F, 5 Q). 2d hierarchical clustering

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How to better do social media text normalization for machine translation?

Pidong Wang Hwee Tou Ng. A Beam-Search Decoder for Normalization of Social Media Text with Application to Machine Translation. How to better do social media text normalization for machine translation? Previous work focused on substituting informal words with their formal forms

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