Search: "Machine learning" Filter:"Machine learning"

19 tagged events, 4 books found.


Tagged events

December 2018

DEC 20
MIKE 2018 is an interdisciplinary conference that brings together researchers and practitioners from the domains of lear...

Cluj-Napoca,
Romania

February 2019

FEB 22
Paper Publication: All accepted papers must be written in English and will be published into #ACM Conference Proceeding...

Zhuhai,
China

September 2019

SEP 20
Lexis invites all the participants across the globe to attend the International conference on Artificial Intelligence du...

Vancouver,
Canada

October 2019

OCT 18
Conference official website:http://www.rsvt.org/ Meeting time:October 18-20, 2019. Meeting place:Wuhan, China. ...

Wuhan,
China

November 2019

NOV 19
[Scopus/Ei Compendex] 2019 International Conference on Machine Learning and Intelligent Systems (MLIS 2019) Website: ...

,
Taiwan, Province Of China

NOV 27
Conference official website:http://www.spml.net Meeting time:November 27-29, 2019. Meeting place:Hangzhou, China...

Hangzhou,
China

June 2020

JUN 7
Intellipaat is organizing a free demo for Machine Learning online course, in this Demo, you will Learn key modules such ...

,
India

JUN 18
The Robot Intelligence Technology & Applications Webinar (iRobot-2020) is the only 100% inclusive, 100% virtual event de...

JUN 20
International Conference on Machine learning and Cloud Computing (MLCL 2020) June 20~21, 2020, Dubai, UAE https://csit...

,
United Arab Emirates

JUN 25
In this modern world of computer science, researchers focus mainly on the integration of Artificial Intelligence and oth...

Secunderabad,
India

July 2020

JUL 20
3rd International Conference on Computer Science & Cloud Computing (Cloud Science-2020), which will be held during July ...

Montreal,
Canada

August 2020

AUG 13
The 6th International Conference on Machine Vision and Machine Learning (MVML’20) aims to become the leading annual co...

September 2020

SEP 10
In this 2-day training, you will learn how to build machine learning applications in C# with Microsoft’s new ML.NET li...

,
Hungary

October 2020

OCT 19
Join the machine learning revolution. O'Reilly TensorFlow World brings together the vibrant and growing ecosystem that'...

Santa Clara,
United States

OCT 24
International Conference on Machine Learning Techniques and NLP (MLNLP 2020) October 24-25, 2020, Sydney, Australia...

,
Australia

February 2021

FEB 4
10th International Conference on Pattern Recognition Applications and Methods ICPRAM website: http://www.icpram.org ...

Vienna,
Austria

June 2021

JUN 25
3rd International Conference on New Approaches in Education, which will be held in Oxford, UK during 25-27 June, 2021. ...

oxford,
United Kingdom

July 2021

JUL 16
The 4th Int'l Conference on Machine Learning, Pattern Recognition and Intelligent Systems (MLPRIS 2021) Conference Date...

Kunming,
China

JUL 29
The 7th International Conference on Machine Vision and Machine Learning (MVML’21) aims to become the leading annual co...

Prague,
Czech Republic

Books

by Derman Akgol & Mehmet Fatih Akay (advisor)

06/26/2017

The purpose of this thesis is to forecast the amount of network traffic in Transmission Control Protocol/Internet Protocol (TCP/IP) -based networks by using different time lags and various machine learning methods including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Neural Network, M5P (a decision tree with linear regression functions at the nodes), Random Forest (RF), Random Tree (RT), and Reduced Error Prunning Error (REPTree), and statistical regression methods including Multiple Linear Regression (MLR) and Holt-Winters and compare the performance of statistical and machine learning methods. Two different Internet Service Providers' (ISPs) traffic data have been utilized to build traffic forecasting models. The first 66% of the data sets has ...

by Mustafa Mikail Ozciloglu & Mehmet Fatih Akay (advisor)

03/02/2017

Upper body power (UBP) is one of the most important factors affecting the performance of cross-country skiers during races. Although some initial studies have already attempted to predict UBP, until now, no study has attempted to apply machine learning methods combined with various feature selection algorithms to identify the discriminative features for prediction of UBP. The purpose of this study is to develop new prediction models for predicting the 10-second UBP (UBP10) and 60-second UBP (UBP60) of cross-country skiers by using General Regression Neural Networks (GRNN), Radial-Basis Function Network (RBF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Single Decision Tree (SDT) and Tree Boost (TB) along with the Relief-F feature selection algorithm, minimum redundancy maxim...

by Gozde Yigit Ozsert & Mehmet Fatih Akay (advisor)

06/28/2017

The purpose of this thesis is to develop new hybrid admission decision prediction models by using different machine learning methods including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Network, TreeBoost (TB) and K-Means Clustering (KMC) combined with feature selection algorithms to investigate the effect of the predictor variables on the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University. Three feature selection algorithms including Relief-F, F-Score and Correlation-based Feature Selection (CFS) have been considered. Experiments have been conducted on the datasets, which contain data of participants who applied to the School in 2006 and 2007. The datasets have been randomly split into train...

Pattern Recognition Methods for Crop Classification from Hyperspectral Remote Sensing Images

Metodos de Clasificacion Aplicados al Reconocimiento de Campos de Cultivo a partir de Imagenes Hiperespectrales

by Luis Gomez Chova

05/21/2004

(Complete work in Spanish) Remote sensing aerial spectral imaging was one of the first application areas where spectral imaging was used in order to identify and monitor the natural resources and covers on earth surface. Aerial spectral imaging is being developed with the aim of monitoring natural resources like coastal areas, forestry and extensive crops. The information contained in hyperspectral images allows the reconstruction of the energy curve radiated by the terrestrial surface throughout the electromagnetic spectrum. Hence, the characterization, identification and classification of the observed material from their spectral curve is an interesting possibility. Pattern recognition methods have proven to be effective techniques in this kind of applications. In fact, classification of...