'Supervised classification' presentation slideshows

Supervised classification - PowerPoint PPT Presentation


Some slide material taken from or inspired by: Groth, Han and Kamber, Cerrito, SAS

Some slide material taken from or inspired by: Groth, Han and Kamber, Cerrito, SAS

DSCI 4520/5240 (DATA MINING). DSCI 4520/5240 Data Mining. Some slide material taken from or inspired by: Groth, Han and Kamber, Cerrito, SAS. Introduction to DM. “It is a capital mistake to theorize before one has data.

By nellis
(215 views)

Data Mining: A Closer Look

Data Mining: A Closer Look

Data Mining: A Closer Look. Chapter 2. 2.1 Data Mining Strategies (p35). Moh!. Classification. Learning is supervised. The dependent variable is categorical. Well-defined classes. Current rather than future behavior. Estimation. Learning is supervised.

By sage
(130 views)

Some slide material taken from or inspired by: Groth, Han and Kamber, Cerrito, SAS

Some slide material taken from or inspired by: Groth, Han and Kamber, Cerrito, SAS

DSCI 4520/5240 (DATA MINING). DSCI 4520/5240 Data Mining. Some slide material taken from or inspired by: Groth, Han and Kamber, Cerrito, SAS. Introduction to DM. “It is a capital mistake to theorize before one has data.

By kenley
(168 views)

Image Classification

Image Classification

Image Classification. Chapter 12. Intro. Digital image classification is assigning pixels to classes (categories) Each pixel has as many digital values as there are bands Compare the values to pixels of known composition and assign them accordingly Each class (in theory) is homogenous.

By therese
(17 views)

Section 1.1

Section 1.1

Section 1.1 . Background. Objectives. Discuss some of the history of data mining. Define data mining and its uses. Defining Characteristics. 1. The Data Massive, operational, and opportunistic 2. The Users and Sponsors Business decision support 3. The Methodology

By isra
(84 views)

An Overview of RS Image Clustering and Classification

An Overview of RS Image Clustering and Classification

An Overview of RS Image Clustering and Classification. by Miles Logsdon with thanks to Robin Weeks Frank Westerlund. What is Remote Sensing and Image Classification?.

By jaden
(201 views)

Data-Intensive Computing with MapReduce

Data-Intensive Computing with MapReduce

Data-Intensive Computing with MapReduce. Session 7: Clustering and Classification. Jimmy Lin University of Maryland Thursday, March 7, 2013.

By darci
(99 views)

Places @ Facebook

Places @ Facebook

Places @ Facebook. Justin Moore 6/12/2014. Our goal is to enable engagement based on a world-class POI database. We believe that competitive quality in the following 5 dimensions is a sufficient basis for competitive product experiences:

By tehya
(83 views)

Frank Falzone Ross Meyer FR 3262 10.December.2012

Frank Falzone Ross Meyer FR 3262 10.December.2012

Identifying Potential Plantation Sites for Hybrid Hazelnut Production in a Micro-Cooperative Context. Frank Falzone Ross Meyer FR 3262 10.December.2012. Outline. Hybrid hazels in context Desired project output Data acquisition Procedures Accuracy assessment Results

By ryo
(62 views)

Classification of Snow Leopard habitat in Mt. Everest National Park

Classification of Snow Leopard habitat in Mt. Everest National Park

Classification of Snow Leopard habitat in Mt. Everest National Park. Sujhav Pun & Martin Gordon. Background. Snow leopards disappeared in the 1960's but in the late 1980’s anecdotal reports noted their return. Very elusive Altitudinal home range 3500-5500m

By benoit
(131 views)

Exercise #5: Supervised Classification

Exercise #5: Supervised Classification

Exercise #5: Supervised Classification. Step 1. Delineating Training Sites and Generating Signatures. A n individual training site is delineated as an “area of interest” and given a class name.

By otylia
(104 views)

Anti-Learning

Anti-Learning

Anti-Learning. Adam Kowalczyk Statistical Machine Learning NICTA, Canberra (Adam.Kowalczyk@nicta.com.au). National ICT Australia Limited is funded and supported by:. 1. Anti-learning Elevated XOR Natural data Predicting Chemo-Radio-Therapy (CRT) response for Oesophageal Cancer

By paniz
(111 views)

Environmental Remote Sensing GEOG 2021

Environmental Remote Sensing GEOG 2021

Environmental Remote Sensing GEOG 2021. Lecture 5 Image classification. Purpose. categorising data data abstraction / simplification data interpretation mapping for land cover mapping

By lara
(92 views)

Readings

Readings

Lectures 11 Analysis and Classification of Digital MSS Data 21 October 2008 Today’s Lecture will be given by Dr. Tatiana Loboda, a Research Scientist in the Department of Geography. Readings. Campbell, Chapters 12,17.9-17.10.

By shiloh
(155 views)

Image Classification

Image Classification

Image Classification. Digital Image Processing Techniques. Image Restoration Image Enhancement Image Classification. Image Classification: the art and science of using the computer to interpret the image. Why do it?.

By chaka
(361 views)

Supervised classification

Supervised classification

Statistical Analysis of Microarray Data. Supervised classification. Application: ALL versus AML (data from Golub et al., 1999). Data source: Golub et al (1999). First historical publication searching for molecular signatures of cancer type. Training set 38 samples from 2 types of leukemia

By cleave
(142 views)

W. Dean Hively 1 , Ali Sadeghi* 1 , Megan Lang 1 , Varaprasad Bandaru 2 , and Greg McCarty 1

W. Dean Hively 1 , Ali Sadeghi* 1 , Megan Lang 1 , Varaprasad Bandaru 2 , and Greg McCarty 1

Natural Resources Conservation Service. Deriving Satellite Crop Rotation Maps for Distributed Modeling of Water Quality in the Choptank River watershed. W. Dean Hively 1 , Ali Sadeghi* 1 , Megan Lang 1 , Varaprasad Bandaru 2 , and Greg McCarty 1 * (301) 504-6693; Ali.Sadeghi@ars.usda.gov

By jody
(176 views)

An Image C lassification of Khartoum, Sudan

An Image C lassification of Khartoum, Sudan

by Lia Sullivan. An Image C lassification of Khartoum, Sudan. Landsat ETM+ 2006 Image of Khartoum. Global Land Cover Facility. www.landcover.org. Delineate the urban extent of Khartoum. Create 5 output classes in the process: urban desert fallow agriculture water

By sue
(72 views)

Integration of sensors for photogrammetry and remote sensing

Integration of sensors for photogrammetry and remote sensing

Integration of sensors for photogrammetry and remote sensing. 8 th semester, MS 2005. Overview on satellites and sensors operating in the optical spectrum. Earth observing system (EOS) Landsat S POT NOAA Other satellite programs Exercise: supervised classification of a Landsat TM image.

By wind
(132 views)

Remote Sensing - I

Remote Sensing - I

Remote Sensing - I. - Optical Range - Image Classification. Image Optimization versus Classification. Landsat-TM of Morocco/Anti-Atlas. Classification Product. Enhanced (optimized) Image Data. Object Space. Feature Space. Result Theme Space. Feature- extraction. Classifier.

By clovis
(210 views)

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