'Random forest' diaporamas de présentation

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Random Forest

Random Forest

Random Forest. Predrag Radenkovi ć 3237/10 Facult y of Electrical Engineering University Of Belgrade. Definition.

By bernad
(1157 views)

Random Forest

Random Forest

Random Forest. Predrag Radenkovi ć 3237/10 Facult y of Electrical Engineering University Of Belgrade. Definition.

By arleen
(268 views)

Duc Hoang – Rhodes College Supervisor: Dr. Gabriel N. Perdue SIST – Final Presentation

Duc Hoang – Rhodes College Supervisor: Dr. Gabriel N. Perdue SIST – Final Presentation

Duc Hoang – Rhodes College Supervisor: Dr. Gabriel N. Perdue SIST – Final Presentation 1 August 2019. Inferring Convolutional Neural Networks’ accuracy from its architectural characterizations. Outline. I. MINERvA experiment Neutrinos and MINERvA detector

By tait
(500 views)

Automated Prevention of Ransomware with Machine Learning and GPOs

Automated Prevention of Ransomware with Machine Learning and GPOs

Automated Prevention of Ransomware with Machine Learning and GPOs. Rod Soto. SPO2-T11. Senior Security Researcher Splunk , Inc. @ rodsoto. Joseph Zadeh. Senior Data Scientist Splunk , Inc. @ josephzadeh. $ Whoami. Rod Soto @ rodsoto

By hayes
(110 views)

Learning to Detect Phishing Emails

Learning to Detect Phishing Emails

Learning to Detect Phishing Emails. Ian Fette Norman Sadeh Anthony Tomasic Presented by – Bhavin Madhani. Authors. Ian Fette : - Masters degree from Carnegie Mellon University. - Product Manager at Google - works on the Google Chrome team .

By redford
(203 views)

Pathway Networks

Pathway Networks

Pathway Networks. The following pictures indicate how the models associated the different pathways. The larger the arrow connecting two pathways, the greater the correlation, according to the p-values. Random Forest. Comparing Pathway 1 and 2. MAP Kinase. Huntington's Disease. Glutathione.

By avongara
(108 views)

Credit Card Fraudulent Transaction Detection

Credit Card Fraudulent Transaction Detection

Credit Card Fraudulent Transaction Detection. As a part of CSC 219: Final Project Presentation Team Members : (Group #10) - Darshit Pandya - Sreeteja K. Guided By : - Dr. Meiliu Lu. Abstract.

By Gabriel
(216 views)

Data Mining For Credit Card Fraud : A Comparative Study

Data Mining For Credit Card Fraud : A Comparative Study

Data Mining For Credit Card Fraud : A Comparative Study. Xxxxxxxx DSCI 5240 | Dr. Nick Evangelopoulos Graduate Presentation. Overview. Credit Card Fraud Data Mining Techniques Data Experimental Setup Results. Credit Card Fraud. Two Types: Application Fraud

By damia
(308 views)

Submit Predictions

Submit Predictions

Goal. Predict whom survived the Titanic Disaster. Hypotheses. Get Data. Data Management. Statistics & Analysis. Correctly Predict Passenger’s Fate . Submit Predictions. Score = . Number of Passengers in Test Dataset. Training and Test Data. Training Data. Develop Model.

By alayna
(175 views)

Nurissaidah Ulinnuha

Nurissaidah Ulinnuha

A Study of Academic Performance using Random Forest, Artificial Neural Network, Naïve Bayesian and Logistic Regression . Nurissaidah Ulinnuha. Introduction. LITERATURE REVIEW. Artificial Neural Network. Superiority

By angus
(137 views)

Bug Prediction for Fine-Grained Source Code Changes

Bug Prediction for Fine-Grained Source Code Changes

Bug Prediction for Fine-Grained Source Code Changes. Zi Yuan, Lili Yu, Chao Liu Software Engineering Institute Beihang University, Beijing, China. Outlines. Introduction Bug Prediction Experiments and Evaluation Threads to Validity Conclusions and Future Work. Introduction.

By zeheb
(124 views)

Boosted Decision Trees, a Powerful Event Classifier

Boosted Decision Trees, a Powerful Event Classifier

Boosted Decision Trees, a Powerful Event Classifier. Byron Roe, Haijun Yang, Ji Zhu University of Michigan. Outline . What is Boosting? Comparisons of ANN and Boosting for the MiniBooNE experiment Comparisons of Boosting and Other Classifiers

By shelley
(177 views)

Detecting human activities using smartphones and maps

Detecting human activities using smartphones and maps

Detecting human activities using smartphones and maps. Leon Stenneth Adviser: Professor Ouri Wolfson Co-Adviser: Professor Philip Yu. Road map. Outdoor transportation mode detection Indoor and outdoor transportation mode detection Parking status detection Parking availability estimation.

By geneva
(136 views)

Flexible generalization of ordinary linear regression.

Flexible generalization of ordinary linear regression.

Random generalized linear model: a highly accurate and interpretable ensemble predictor Song L, Langfelder P, Horvath S. BMC Bioinformatics 2013 Steve Horvath ( shorvath@mednet.ucla.edu ) University of California, Los Angeles. Linear. Logistic. Multi- nomial. Poisson.

By darin
(135 views)

Activity recognition with wearable accelerometers

Activity recognition with wearable accelerometers

Activity recognition with wearable accelerometers. Mitja Luštrek. Jožef Stefan Institute Department of Intelligent Systems Slovenia. Tutorial at the University of Bremen, November 2012. Outline. Accelerometers Activity recognition with machine learning

By dalila
(193 views)

Cellular Pathway Mapping Using Gene Expression Profiles and Upstream Elements

Cellular Pathway Mapping Using Gene Expression Profiles and Upstream Elements

Cellular Pathway Mapping Using Gene Expression Profiles and Upstream Elements. Presentation By: Melanie Smith. Overview. Goal: Identify Gene Function Tool: Random Forest Classification Model Summer 1: Gene Expression Profiles

By winola
(84 views)

Facial Recognition in Biometrics

Facial Recognition in Biometrics

Susan Simmons University of North Carolina Wilmington. Facial Recognition in Biometrics. Biometrics. Biometrics (wikipedia) -- Biometrics are used to identify the identity of an input sample when compared to a template, used in cases to identify specific people by certain characteristics.

By latika
(190 views)

Kdd Cup 2013 Author Paper Identification Final Report

Kdd Cup 2013 Author Paper Identification Final Report

Kdd Cup 2013 Author Paper Identification Final Report. Ben Deng – M10112006. Outline. Problem Description Database Analysis Research Issue Proposed Ideas Results. Problem Description. Inside the research community, it has more than 50 million publications and 19 million authors.

By amity
(273 views)

Measurement of B + K + νν

Measurement of B + K + νν

California Institute of Technology Department of Energy Review July 24, 2007. Measurement of B + K + νν. David Doll Ilya Narsky David Hitlin. Theory.

By kato
(116 views)

Random Forest and Graph Cut based segmentation of human limbs

Random Forest and Graph Cut based segmentation of human limbs

Random Forest and Graph Cut based segmentation of human limbs. Nadezhda Zlateva , IICT-BAS. 7 Sept. 2011. Outline. Human Pose Recognition Case Study Randomized Decision Tree Random Forest Experimental results with RF Graph Cut Experimental results with GC

By devika
(268 views)

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