HOME QUICK SEARCH AUTHORS KEYWORDS HELP BUGS

training set

213 papers and 9 presentations; 209 titles.

Back to keywords

Conference × Year × Title × Authors Extras Pages
WUSS 2026 From Prediction to Explanation: Per-Patient SHAP Attribution for a Random Forest Diabetes Risk Classifier Leon Rod Davoody,
Chary Akmyradov		 source 12
PharmaSUG CN 2026 Automated Clinical Text Classification with Robust Machine Learning for Pharmaceutical Data Curation Weineng Zhou source 11
PharmaSUG 2026 From Automation to Evidence: Governing LLMs and AI Agents in Real-World Outcomes Research Sherrine Eid source 17
PHUSE US 2026 Coding Clinical R with Confidence: Digital AI Assistant based on Self-Hosted LLMs Kostiantyn Drach source
presentation source		 8
PHUSE US 2026 Orchestrating Multi-Agent Workflows for Complex Trial Data Integration Rajesh Hagalwadi source 8
PHUSE APAC 2026 A Hybrid Machine Learning Framework for Predicting Himanshu Dadhich,
Nikhil Khandelwal		 source
presentation source		 26
PHUSE APAC 2026 An Agentic Approach to Validation, Influencing Reporting with AI Junibel R. De Leon,
Karthik Palani		 source
presentation source		 5
PHUSE EU 2025 How to Identify and Operationalize GenAI Use Cases Which Really Help You Christoph Bergen,
Luis Wirth		 source
presentation source		 8
PHUSE EU 2025 Leveraging LLM for R Shiny App Code Development: A Case Study with Event Prediction [presentation] Yirong Cao source 15
MWSUG 2025 Feature Selection and Classification Using Fuzzy Logic Ross Bettinger source 39
SESUG 2025 Introduction to Developing Neural Nets David B. Horvath 21
WUSS 2025 Future trends in AI and impacts on SAS Programming David B. Horvath source 5
PharmaSUG 2025 Methodology for AI-driven Outcome Prediction for Patients with Atrial Fibrillation After Transcatheter Aortic Valve Implantation (TAVI) Felix Just,
Krishna Padmanabhan,
Parth Jinger,
Amanda Borrow,
Rüdiger Smolnik,
Eva-Maria Fronk,
Neelam Yadav,
Sergei Krivtcov,
Stas Zakharkin,
Martin Unverdorben,
Asmir Vodencarevic		 source 6
PharmaSUG 2025 Super Learner for Predictive Modeling and Causal Analysis Honghe Zhao,
Clay Thompson,
Michael Lamm		 source 9
PHUSE US 2025 Can AI Help Overcome the Validation Burden for Open Source? Marc Jantke source
presentation source		 8
PHUSE US 2025 Dismantling the Black Box: Why We Need to Think Bigger About AI Integration Christina Dinger,
Joe Lebers		 source
presentation source		 7
PHUSE US 2025 LLMs in Clinical Trials: Overcoming Challenges Related to Patient Confidentiality and Intellectual Property Amit Javkhedkar,
Samip Shah		 source
presentation source		 7
MWSUG 2024 Charting Your Organization's Machine Learning Roadmap Ryan Paul Lafler source 7
MWSUG 2024 Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow Ryan Paul Lafler,
Anna T. K. Wade		 source 18
PHUSE EU 2024 Accelerating GSK's Open-Source Journey with The AI Programmer Andy Nicholls,
UK Christina Fillmore		 source
presentation source		 7
PHUSE EU 2024 Biomedical Question Answering with Large Language Models Matthew Moody source
presentation source		 10
PHUSE EU 2024 The Magical Trinity of TLF Automation: Digital Documents, Metadata, and GenAI Alastair Scarlett source
presentation source		 4
SESUG 2024 Charting Your Organization's Machine Learning Roadmap Ryan Paul Lafler source 7
SESUG 2024 Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow Ryan Paul Lafler,
Anna T. K. Wade		 source 18
SESUG 2024 Introduction to Developing Neural Nets David B. Horvath source 17
SESUG 2024 Unlocking Insights: Harnessing Supervised Machine Learning for Advanced Data Analysis Vindhya Hegde source 23
WUSS 2024 Charting Your Organization's Machine Learning Roadmap Ryan Paul Lafler source 6
WUSS 2024 Developing Artificial and Convolutional Neural Networks with Python's Keras API for TensorFlow Ryan Paul Lafler,
Anna T. K. Wade		 source 18
WUSS 2024 From Keys to Credit: A Deep Dive into Homeownership's Influence on Loan Quality Yi-Che Chen,
Debanik Chakraborty		 source 15
WUSS 2024 Training Domain Specific AI for Clinical Data: Developing an AI Agent to Transform EDC Data to CDISC SDTM and then to ADaM Standards Sy J. Truong source 12
WUSS 2024 X-Ray Image Classification with Neural Networks Riley Rutan,
Eric C Braga,
Xinyu Du		 source 10
PharmaSUG 2024 Bayesian Additive Regression Trees for Counterfactual Prediction and Estimation Michael Lamm source 10
PharmaSUG 2024 MLNR or Machine Learning in R Chuck Kincaid source 23
PharmaSUG 2024 Unraveling the Layers within Neural Networks: Designing Artificial and Convolutional Neural Networks for Classification and Regression Using Python-s Keras & TensorFlow Ryan Paul Lafler,
Anna T. K. Wade		 source 18
PHUSE US 2024 A Holistic Approach to Trustworthy Artificial Intelligence (AI) Tara Chavda,
Qingying Ally Lu		 source
presentation source		 6
PHUSE US 2024 Beyond the Hype: A Pragmatic Approach to Leveraging AI/ML for Data Review Robert Musterer source
presentation source		 10
PHUSE US 2024 Risk-Based Monitoring Using AI/ML Detection of Systematic Bias in Clinical Data Kostiantyn Drach,
Sergey Glushakov		 source
presentation source		 12
PHUSE EU 2023 Generative AI: A Case Study Highlighting the Potential and the Risks Nikolay Manchev,
Michael Harnish		 source
presentation source		 23
PHUSE EU 2023 GPT (Generative Pretrained Transformers) and LLM (Large Language Models) have generated a lot of attention since their inception in early 2023 with the advent of OpenAI GPT3.5, then GPT4.0 Jerome Vetillard,
Mark Lambrecht		 source
presentation source		 16
PHUSE EU 2023 Unleashing the Power of Large Language Models for Clinical Data Approaches: Will AI chatbots like ChatGPT/Bard replace statistical programmer and statisticians over time? Ercan Suekuer source
presentation source		 9
PHUSE EU 2023 Using Machine learning for building a demonstrative model to counter the under-diagnosis of dementia Ashapriya Sebastian source
presentation source		 12
WUSS 2023 Introduction to Machine Learning - Descriptions and Best Practices Jim Box source 13
WUSS 2023 Leveraging Logistic Regression and Bootstrap Sampling to Assess a Decision Threshold in Classification Predictive Analytics Colleen E. McGahan source 15
WUSS 2023 Unraveling the Layers within Neural Networks: Designing Artificial and Convolutional Neural Networks for Classification and Regression Tasks using TensorFlow in Python Ryan Paul Lafler,
Anna T. K. Wade		 source 24
SESUG 2023 Designing Against Bias: Identifying and Mitigating Bias in Machine Learning and AI David J. Corliss source 9
PharmaSUG 2023 What is Machine Learning, anyway? Jim Box source 11
PHUSE US 2023 Introduction to Machine Learning Models, How to Train Them and What to Do if They Become Sassy [presentation] Thomas Kalfas source 34
PHUSE EU 2022 If you want to deliver standardised clinical data sets quickly, talk to the machine Chris Austin,
Michael Phillips		 source
presentation source		 6
SESUG 2022 Don't be so One-Dimensional: How to Engineer Multi-Dimensional, High Cardinality Categorical Inputs for Machine Learning Aleksandar Nikolic source 25
WUSS 2022 What's your model really doing? Understanding human biases in Machine Learning. Jim Box source 5
PharmaSUG 2022 Simple and Efficient Bootstrap Validation of Predictive Models Using SAS/STAT Software Matthew T. Slaughter,
Isaiah Lankham		 source 22
PharmaSUG 2022 What-s your model really doing? Human bias in Machine Learning Jim Box source 4
PHUSE EU 2021 Deep Active Learning for Clinical Entity Recognition [presentation] Nadia Abraham source
video		 33
PHUSE US 2021 Standing Room Only Presentation: ML08: You Are the Company You Keep: Converting Words to Numbers Using Text Embeddings [presentation] Michael Stackhouse source 23
PharmaSUG 2021 Random Forest Machine Learning Application using R Shiny for Project Timeline Forecasting Hrideep Antony,
Aman Bahl		 source 8
SGF 2021 A Framework for Simple and Efficient Bootstrap Validation in SAS, with Examples Isaiah Lankham,
Matthew T. Slaughter		 source 22
PHUSE EU 2020 AI-supported and Demand-oriented Data Integration of Clinical Studies [presentation] David Hipp,
Kai Spielmann		 source
video		 18
PHUSE EU 2020 Utilising Automation for Mass Model Creation to Improve the Economics of Machine Learning Projects [presentation] Alex Tsui source
video		 18
SESUG 2020 Model Comparison of Machine Learning Techniques to detect signs of Penetrating Abdominal and Pelvic Injuries using Base JMP and R Integration Melvin T. Alexander source 25
SGF 2020 Apply the Key Ideas from Andrew Ng's Machine Learning Certification (Coursera) in SAS Viya Petri Roine source 9
SGF 2020 Bringing Computer Vision to the Edge: An Overview of Real-Time Image Analysis with SAS Maggie Du source 15
SGF 2020 Deploying Computer Vision by Combining Deep Learning Action Sets with Open-Source Technology Jonny McElhinney,
Duncan Bain,
Haidar Altaie		 source 20
SGF 2020 Exploring Online Drug Reviews using Text Analytics, Sentiment Analysis, and Data Mining Models Thu Dinh,
Goutam Chakraborty		 source 23
SGF 2020 How to Build a Text Analytics Model in SAS Viya with Python Nate Gilmore source 13
SGF 2020 How to Explain Your Black-Box Models in SAS Viya Funda Güneş source 26
SGF 2020 Human Bias in Machine Learning: How Well Do You Really Know Your Model? Jim Box source 14
SGF 2020 Neural-Network Based Forecasting Strategies in SAS Viya Steven C. Mills source 14
SGF 2020 The Seven Most Popular Machine Learning Algorithms for Online Fraud Detection and Their Use in SAS Patrick Maher source 6
SGF 2020 Transfer Learning for Mining Digital Phenotype by SAS Viya Satoki Fujita source 18
SGF 2020 Turning the Crank. A Simulation of Optimizing Model Retraining David R. Duling source 17
SGF 2020 Using Analytics to Predict Tax Recovery and Prioritize Audits and Investigations in Canada Jason A. Oliver source 19
SGF 2020 Using Python with Model Studio for SAS Visual Data Mining and Machine Learning Jagruti Kanjia source 35
SGF 2020 What's New in SAS Visual Data Mining and Machine Learning: From a Programmer's Perspective Tao Wang source 13
PharmaSUG 2020 Detecting Side Effects and Evaluating the Effectiveness of Drugs from Customers' Online Reviews using Text Analytics, Sentiment Analysis and Machine Learning Models Thu Dinh,
Goutam Chakraborty		 source 22
PHUSE US 2020 A Comparison of Machine Learning Algorithms for Binary Classification of Free-text Fields Using SAS, R, and Python Michael S Rimler source
presentation source		 12
PHUSE US 2020 Data Review of RECIST Criteria using ML/AI A Proof of Concept Example Priya Gopal source
presentation source		 7
PHUSE US 2020 Metadata-based Auto-programming Process - Part 3: Virtual AI Assistant to Automate - TLG Generation Harry Chen,
Shobha Rani		 source
presentation source		 14
PHUSE EU 2019 Do You Know What Your Model is Doing? A Look at How Human Bias Influences Machine Learning Jim Box,
Elena Snavely		 source
presentation source		 3
PHUSE EU 2019 Using R to build artificial neural networks in medical data Thomas Wollseifen source
presentation source		 17
SESUG 2019 A Random Forest Example of the Boston Housing Data using the Base SAS and the PROC_R macro in SAS Enterprise Guide Melvin T. Alexander source 18
SESUG 2019 Glass Box Neural Networks Ross Bettinger source 14
SESUG 2019 Hybrid Rare Event Sampling Technique Ross Bettinger source 15
SESUG 2019 Summarizing some conventional methods to classify a binary target Yida Bao,
Philippe Gaillard		 source 10
SESUG 2019 Using PROC FCMP to Implement Anomaly Detection Method Tsung-Hsun Tsai source 6
SCSUG 2019 Building a Neural Network Scoring Model with JMP and Microsoft SQL Server to predict Aggregate Motor Vehicle Driver Alcohol Impairment David Whitchurch source 10
MWSUG 2019 Detecting Side Effects and Evaluating Effectiveness of Drugs from Customers' Online Reviews using Text Analytics and Data Mining Models Thu Dinh 23
MWSUG 2019 Evaluate your SCORE: Logistic regression prediction comparison using the SCORE statement Robert G. Downer 6
MWSUG 2019 Logistic Regression, Basics and Beyond Bruce S. Lund 35
PharmaSUG 2019 An Application of Supervised Machine Learning Models on Natural Language Processing: Classifying QC Comments into Categories Xiaoyu Tang,
Todd Case		 source 10
PharmaSUG 2019 Hands-on Training for Machine Learning Programming Kevin Lee source 12
PharmaSUG 2019 Machine Learning Approaches to Identify Rare Diseases Xuan Sun,
Ruohan Wang		 source 5
PharmaSUG 2019 Performing Analytics on Free-Text Data Fields: A Programer-s Wurst Nitemare Michael S Rimler,
Matt Pitlyk		 source 18
PharmaSUG 2019 Text Analysis of QC/Issue Tracker Using Natural Language Processing (NLP) Tools Huijuan Zhang,
Todd Case		 source 8
SGF 2019 Asking the Right Questions: A study in Multiclass Imbalanced Classification for H1-B Visas Matthew Bunker,
Owen Kelly		 source 7
SGF 2019 Machine Learning and Predictive Analytics in SAS Enterprise Miner and SAS/STAT Software David Richard Cutler source 21
SGF 2019 Monitoring the Relevance of Predictors for a Model Over Time Ming-Long Lam source 16
SGF 2019 SAS Enterprise Miner and SAS Visual Data Mining and Machine Learning Hand Shake Jagruti Kanjia,
Dominique Latour,
Holly Sweeney		 source 35
SGF 2019 The Aftermath What Happens after you Deploy Your Models and Decisions David R. Duling source 18
SGF 2019 The Anatomy of an Artificial Intelligence Solution for Automation of Underwriting Decision-Making Tanya Kolosova source 17
PHUSE US 2019 Metadata-based Auto-Programming Process - Part 2: Map Human Language to SAS Code by Natural Language Processing (NLP) Sumesh Kalappurakal,
Harry Chen		 source
presentation source		 11
PHUSE US 2019 Voice-based Clinical Monitoring Assistant Anoop Ambika source
presentation source		 7
SCSUG 2018 Machine Learning in Litigation Prediction - Workers' Compensation Litigation Propensity Predictive Model Mei Najim source 17
PHUSE EU 2018 Introduction to Machine Learning Nicolas Dupuis,
Kevin Lee		 source
presentation source		 9
PHUSE EU 2018 Want to Get Started with Deep Learning? Prototyping a Deep Learning Image Classifier Thomas Ellebaek source
presentation source		 9
SESUG 2018 Predicting the Risk of Attrition for Undergraduate Students using SAS Enterprise Miner Sivateja Reddy Kandula source 12
WUSS 2018 Deep neural nets in SAS Enterprise Miner Russell M. Lavery source 49
WUSS 2018 The Anatomy of Artificial Intelligence Solution for Automation of Underwriting Decision-Making Process. Samuel Berestizhevsky,
Tanya Kolosova		 source 16
PHUSE US 2018 A Semantic NLP Approach for Structuring and Analysis of FDA Meeting Minutes Documents Michelle Shen,
Weizhong Zhao		 source
presentation source		 12
PHUSE US 2018 Feature Selection for Predictive Modelling - A Needle in a Haystack Problem Sudipta Basu source
presentation source		 6
PHUSE US 2018 Mine the Gap: How the Role of Data Scientist Fills a Need in the Pharmaceutical Industry Michael S Rimler,
Jorine Putter		 source
presentation source		 8
PHUSE US 2018 Modeling and Text Analysis to Empower FAERS Adverse Event Mitigation Qais Hatim,
Emily McRae		 source
presentation source		 24
PHUSE US 2018 Upholding Ethics and Integrity: A macro-based approach to detect plagiarism in programming Sridhar Vijendra,
Praveen Kumar		 source
presentation source		 7
PharmaSUG 2018 Build Models without Code with the new SAS Visual Interface Jim Box source 12
SGF 2018 Bayesian Networks for Causal Analysis John Amrhein,
Fei Wang		 source 11
SGF 2018 Claim Analytics: A Litigation Prediction Case Study Mei Najim source 18
SGF 2018 Evaluation of Different Approaches to Reject Inference: a case study in Credit Risk Sergiu Luca source 9
SGF 2018 How Good is That Forecast? The Nuances of Prediction Evaluation Across Time Aric LaBarr source 7
SGF 2018 Interpreting Black-Box Machine Learning Models Using Partial Dependence and Individual Conditional Expectation Plots Ray Wright source 19
SGF 2018 Mitigating the Effects of Class Imbalance Using SMOTE and Tomek Link Undersampling in SAS Kyle Biron source 20
SGF 2018 Model Life Cycle Automation Using REST APIs Wenjie Bao source 17
SGF 2018 New Ways to Incorporate Continuous Predictors in Generalized Linear Models Anthony Salis,
Angela Wu,
Gordon Johnston		 source 15
SGF 2018 Penalized Variable Selection and Quantile Regression in SAS : An Overview Russell M. Lavery source 28
SGF 2018 Retail Product Bundling: A New Approach Bruno Nogueira source 18
SGF 2018 Robust Principal Component Analysis: Two Analyses in One Anthony Salis,
Zohreh Asgharzadeh,
Kyungduck Cha		 source 11
SESUG 2017 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey source 25
MWSUG 2017 Claim Analytics Mei Najim 18
SGF 2017 A Moment-Matching Approach for Generating Synthetic Data in SAS Brittany Bogle,
Jared Erickson		 source 17
SGF 2017 Building Bayesian Network Classifiers Using the HPBNET Procedure Ye Liu,
Weihua Shi,
Wendy Czika		 source 19
SGF 2017 How Members Really Feel About Your Credit Card Anirban Chakraborty,
Surya Bhaskar Ayyalasomayajula		 source 25
SGF 2017 Identifying Abnormal Equipment Behavior and Filtering Data near the Edge for IoT Applications Ryan Gillespie,
Saurabh Gupta		 source 5
SGF 2017 Random Forests with Approximate Bayesian Model Averaging Tiny Du Toit,
Andre De Waal		 source 11
SGF 2017 Real AdaBoost: Boosting for Credit Scorecards and Similarity to WOE Logistic Regression Paul Edwards source 12
SGF 2017 The Three Amigos Factors: Determining Term Deposit Purchases: How a Bank Can Get Other People's Money Gina Colaianni,
Bogdan Gadidov,
Matthew Mitchell		 source 59
SCSUG 2016 Determining the functionality of Water Pumps in Tanzania Using SAS EM and VA Indra Kiran Chowdavarapu,
Vivek Damodaran Manikandan		 source 9
SESUG 2016 Factors of Multiple Fatalities in Car Crashes Bill Bentley,
Gina Colaianni,
Kennedy Onzere,
Cheryl Joneckis,
Xuelei (Sherry) Ni		 source 62
MWSUG 2016 An Animated Guide: Deep Neural Networks in SAS Enterprise Miner Russell M. Lavery 47
MWSUG 2016 An Animated Guide: Penalized variable selection techniques in SAS and Quantile Regression Russell M. Lavery,
Peter Flom		 24
WUSS 2016 Social Media, Anonymity, and Fraud: HP Forest Node in SAS Enterprise Miner Taylor K. Larkin,
Denise J. McManus		 source 5
SGF 2016 Analysis of IMDb Reviews for Movies and Television Series Using SAS Enterprise Miner and SAS Sentiment Analysis Studio Ameya Jadhavar,
Goutam Chakraborty,
Prithvi Raj Sirolikar		 source 17
SGF 2016 Factors of Multiple Fatalities in Car Crashes Bill Bentley,
Gina Colaianni,
Cheryl Joneckis,
Sherry Ni,
Kennedy Onzere		 source 60
SGF 2016 Forecasting Short Time Series Using SAS Forecast Server Cobey Abramowski,
Christian Haxholdt,
Chris Houck,
Benjamin Perryman		 source 5
SGF 2016 Getting More from the Singular Value Decomposition (SVD): Enhance Your Models with Document, Sentence, and Term Representations Russell Albright,
James A. Cox,
Ning Jin		 source 15
SGF 2016 Holiday Demand Forecasting in the Utility Industry Rain Xie,
Alex Chien		 source 13
SGF 2016 Improving Performance of Memory-Based Reasoning Model by Using Weight of Evidence Coded Categorical Variables Vinoth Kumar Raja,
Goutam Chakraborty,
Vignesh Dhanabal		 source 6
SGF 2016 Predicting Forest Fire Occurrence and Incremental Fire Rate Using SAS 9.4 and SAS Enterprise Miner 14.1 Quyen Nguyen source 12
SGF 2016 Predicting Human Activity Sensor Data Using an Auto Neural Model with Stepwise Logistic Regression Inputs Venkata Vennelakanti source
presentation		 10
MWSUG 2015 Fitting and Evaluating Logistic Regression Models Bruce S. Lund 26
MWSUG 2015 Shifting Expectations: A practical algorithm for detecting level shifts using robust rolling statistics Matthew Bates 11
WUSS 2015 Introduction to SAS Quantile Regression (PROC QuantSelect and PROC QuantReg ) Peter L. Flom source 25
WUSS 2015 Managing and Monitoring Statistical Models Nathaniel Derby source 13
SGF 2015 A Case Study: Improve Classification of Rare Events with SAS Enterprise Miner Ruizhe Wang,
Novik Lee,
Yun Wei		 source
zip		 12
SGF 2015 SAS and SAP Business Warehouse on SAP HANA--What's in the Handshake? Tatyana Petrova source 17
SGF 2015 Time Series Modeling and Forecasting--An Application to Stress-Test Banks Ania Supady source 13
PHUSE EU 2014 Exploring Safety and Diagnostic Data using Self-Organising Maps Colin de Klerk source
presentation source		 9
WUSS 2014 Decision Trees as a Predictive Modeling Method Gerry Hobbs source 8
SGF 2014 An Overview of Machine Learning with SAS Enterprise Miner Patrick Hall,
Jared Dean,
Ilknur Kaynar Kabul,
Jorge Silva		 source
zip		 24
SGF 2014 Building Gamer Segmentation in the Credit Card Industry Using SAS Enterprise Guide Yang Ge source 10
SGF 2014 Introduction to SAS Decision Management Steve Sparano,
Charlotte Crain,
David R. Duling		 source 18
WUSS 2013 Decision Trees as a Predictive Modeling Method Gerry Hobbs source 8
SESUG 2013 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey source 16
SESUG 2013 What Do Your Consumer Habits Say About Your Health? Using Third-Party Data to Predict Individual Health Risk and Costs Satish Garla source 15
MWSUG 2013 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey 16
MWSUG 2013 Managing and Monitoring Statistical Models Nathaniel Derby 13
NESUG 2013 An Animated Guide: An Introduction to Neural Nets: Russell M. Lavery 12
SGF 2013 A Data-Driven Analytic Strategy for Increasing Yield and Retention at Western Kentucky University Using SAS Enterprise BI and SAS Enterprise Miner Matt T. Bogard source 12
SGF 2013 Extension Node to the Rescue of the Curse of Dimensionality via Weight of Evidence (WOE) Recoding Andrew Cathie,
Goutam Chakraborty,
Satish Garla		 source 9
SGF 2013 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey source 16
SGF 2013 Managing and Monitoring Statistical Models Nathaniel Derby source 13
SGF 2013 Predictive Modeling in Sports Leagues: An Application in the Indian Premier League Goutam Chakraborty,
Pankush Kalgotra,
Ramesh Sharda		 source 10
SGF 2013 Segmentation and Classification Analysis Using SAS Rachel Poulsen source 14
SGF 2013 What Do Your Consumer Habits Say About Your Health? Using Third-Party Data to Predict Individual Health Risk and Costs Albert Hopping,
Rick Monaco,
Sarah Rittman,
Satish Garla		 source 15
NESUG 2012 An Animated Guide: Regression Trees in JMP and Enterprise Miner Russell M. Lavery 32
NESUG 2012 K-Nearest Neighbor Classification and Regression in SAS Liang Xie 16
SESUG 2012 K-Nearest Neighbor Classification and Regression using SAS Liang Xie source 16
WUSS 2012 Multivariate Statistical Analysis in SAS: Segmentation and Classification of BehavioralData Rachel Poulsen source 14
SGF 2012 Its About Time: Discrete Time Survival Analysis using SAS Enterprise Miner Sascha Schubert,
Susan Haller,
Taiyeong Lee		 source 15
SGF 2012 Using PROC GAM to Forecast Claims Reserves in the Runoff Triangles Ling Huang source 7
WUSS 2010 Decision Trees as a Predictive Modeling Method Gerry Hobbs source 7
WUSS 2010 Logit-based Neutral Zone Classification Algorithm with Application to Microbial Community Profiling Rebecca Le source 9
SGF 2010 Rapid Predictive Modeling for Customer Intelligence Wayne Thompson,
David R. Duling		 source 12
SGF 2010 Using the SAS System and SAS Enterprise Miner for Data Mining: A Study of Cancer Survival at the Mayo Clinic Leonard Gordon source 6
WUSS 2009 Dashboard Reports for Predictive Model Management Diane Hatcher source 11
WUSS 2009 Discriminant Analysis, a Powerful Classification Technique in Predictive Modeling George C. J. Fernandez source 20
SGF 2009 Applications of the GLMSELECT Procedure for Megamodel Selection Robert A. Cohen source 25
SGF 2009 Dashboard Reports for Predictive Model Management Jifa Wei,
Emily Y. Gao,
Frank J. Wang,
Robert Chu		 source 12
MWSUG 2008 Chemical Informatics using JMP PowerMV and ChemModLab S. Stanley Young,
Thomas H. Burger,
Michael S. Lajiness,
Wolf-D. Ihlenfeldt		 40
MWSUG 2008 Classification of Breast Cancer Cells Using JMP Marie Gaudard 34
SGF 2008 Coming to a Theater Near You! Sentiment Classification Techniques Using SAS Text Miner Jake Bartlett,
Russell Albright		 source 9
SGF 2008 It's 9:00 AM: Do You Know Where Your Critical Talent Is? Retention Analytics for Human Capital Management Lynn Creecy,
Bradley W. Klenz		 source 11
NESUG 2007 Using PROC PLS (Partial Least Squares) to Build a Gene Prognosis Profile Chenwei Liu 5
MWSUG 2007 Best Practices for Managing Predictive Models in a Production Environment Robert Chu 10
MWSUG 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga 20
WUSS 2007 Data Mining Using SAS Enterprise Miner Randall Matignon source 10
SGF 2007 Best Practices for Managing Predictive Models in a Production Environment Robert Duling,
David M. Thompson,
Wayne Chu		 source 10
SGF 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga source 20
SGF 2007 Improving Credit Scoring by Generalized Additive Model Wensui Cela,
Jimmy Liu		 source 9
SCSUG 2006 What's New in SAS Enterprise Miner 5.2 Wayne Thompson,
David R. Duling		 10
WUSS 2006 Find the Best Prospects for a New Product by Using a Data Mining Model Ting Wu source 8
SUGI 2006 What's New in SAS Enterprise Miner 5.2 Wayne Thompson,
David R. Duling		 source 10
MWSUG 2005 Comparison Of Enterprise Miner And SAS/STAT For Data Mining Patricia B. Cerrito 12
SUGI 2005 Decision Tree Validation: A Comprehensive Approach Sylvain Tremblay source 8
NESUG 2004 A SAS Macro for Naive Bayes Classification Vadim Pliner 6
WUSS 2003 A SAS Macro Solution for Scoring Test Data with Output Features and Parameters from Logistic Regression Model Training Joseph Blue source 5
SEUGI 2003 Towards an automated procedure for annotation of gene products through SAS Methods [presentation] Henrik Tveit source 26
PharmaSUG 2003 Statistical Processing and Analysis of Proteomic and Genomic Data Mark Carpenter,
Sreelatha Melath,
Sijian Zhang,
William E. Grizzle		 4
SEUGI 2002 SAS Enterpise Miner: What does the future hold? [presentation] David R. Duling,
Sascha Schubert		 source 45
SUGI 2002 Discriminant Analysis, A Powerful Classification Technique in Data Mining George C. J. Fernandez source 9
SUGI 2002 Using SAS Enterprise Miner for Data Quality Monitoring in the Veterans Health Administration's External Peer Review Program James H. Forsythe source 5
WUSS 2001 Discriminant Analysis, A Powerful Classification Technique in Data Mining George C. J. Fernandez source 9
SEUGI 2000 Genetic Data Analysis with Enterprise Mineer [presentation] John C. Brocklebank,
Wayne Thompson		 source 24
SUGI 2000 Using Enterprise Miner to Explore and Exploit Drug Discovery Data Michael S. Lajiness source 8
NESUG 1999 Input Selection, Performance Evaluation, and Credit Risk Analysis Shirley Tsai,
David Hamilton		 10
WUSS 1998 A Competitive Bid Pricing System Using JMP William R. Gjertsen source 8
SUGI 1998 Finding the Solution to Data Mining: A Map of the Features and Components of SAS Enterprise Miner Software Table of Contents John C. Brocklebank,
Mark Brown		 source 24
NESUG 1997 Implementation and Comparison of the Back Propagation Neural Network in SAS Software John S. DaPonte 10
SUGI 1997 How Many Good Fishes Are There in Our Net? Neural Networks as a Data Analysis Tool in CDE-Mondadori's Data Warehouse Guido Cuzzocrea,
Alberto Saccardi,
Giovanni Lux,
Emilia Porta,
Arianna Benatti		 source 6
SEUGI 1996 A network to solve your problems Martin Duffy source 7
SEUGI 1996 Neural Networks Application: a case study Rui Alexandre Henriques Gonçalves source 7
SEUGI 1994 Neural Networks and Statistical Models Derek Powell,
Martin Duffy		 source 9
SUGI 1994 Neural Networks and Statistical Models Warren S. Sarle source 13
SUGI 1989 Estimation of Error Rates in Discriminant Analysis by Bootstrapping Jacques Delince,
F. Javier Gallego		 source 5
SUGI.ONE 1976 Discriminant or Cluster Analysis Paul D. Hopkins,
Harold F. Huddleston		 source 11

Back to keywords