'Model selection' presentation slideshows

Novel representations and methods in text classification

1.15k views • 91 slides

Applied Linear Regression

1.12k views • 69 slides

Texas Transformation Project

804 views • 65 slides

Model Selection/Comparison

947 views • 63 slides

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning. Chapter 1: Introduction. Example. Handwritten Digit Recognition. Polynomial Curve Fitting . Sum-of-Squares Error Function. 0 th Order Polynomial. 1 st Order Polynomial. 3 rd Order Polynomial. 9 th Order Polynomial. Over-fitting.

916 views • 59 slides

An Overview of Learning Bayes Nets From Data

689 views • 46 slides

Quantitative Methods

Quantitative Methods. Checking the models II: the other three assumptions. TREATMNT Coef 1 ? 1 BACAFTER = m + b ? BACBEF + 2 ? 2 + ?

451 views • 36 slides

Load Estimation Models

Load Estimation Models. Existing loads come from:. Point-source discharges (NPDES facilities) Info is available on the discharges (DMRs, etc.) Some are steady-flow, others are precip-driven Nonpoint sources All are (mostly) precipitation-driven

498 views • 35 slides

Day 6 Model Selection and Multimodel Inference

Day 6 Model Selection and Multimodel Inference . Topics. Model selection: how do you choose between (and compare) alternate models? Multimodel inference: how do you combine information from more than 1 model?. Topics. Model comparison. Evidence ratios. Likelihood ratio tests.

744 views • 31 slides

Multiple Logistic Regression

Multiple Logistic Regression. RSQUARE, LACKFIT, SELECTION, and interactions. Introduction. Just as with linear regression, logistic regression allows you to look at the effect of multiple predictors on an outcome.

1.03k views • 28 slides

A Composable Simulation Environment for Mechatronic Systems

A Composable Simulation Environment for Mechatronic Systems. Antonio Diaz-Calderon Christiaan J. J. Paredis Pradeep K. Khosla Carnegie Mellon University Work sponsored by DARPA RaDEO program and CMU 1999 European Simulation Symposium October 26-28, 1999, Erlangen, Germany. Hydraulic

528 views • 27 slides

DCM Advanced, Part II

DCM Advanced, Part II. Will Penny (Klaas Stephan) Wellcome Trust Centre for Neuroimaging Institute of Neurology University College London. SPM Course 2014 @ FIL. Overview. Extended DCM for fMRI: nonlinear, two-state, stochastic Embedding computational models in DCMs

838 views • 25 slides

Decision Tree Pruning

Decision Tree Pruning . Problem Statement. We like to output small decision tree Model Selection The building is done until zero training error Option I : Stop Early Small decrease in index function Cons: may miss structure Option 2: Prune after building. Pruning. Input: tree T

807 views • 24 slides

rationale

rationale. existing system VS culture as a function propagated by the masses generate predictive data increase public awareness for the arts increase responsibility of grant makers. Sub-domain examples. Cinema Museum exhibition City Art Project/Installation. film festival.

567 views • 20 slides

Model Uncertainty and Model Selection

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Model Evaluation and Selection

Model Evaluation and Selection. Example Objective: Demonstrate how to evaluate a single model and how to compare alternative models. Evaluating the Sufficiency of a Single Model (followup to example of Mediation Test). When this model is run, a variety of

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Photography

817 views • 10 slides

6.1.4 AIC, Model Selection, and the Correct Model

6.1.4 AIC, Model Selection, and the Correct Model. Any model is a simplification of reality If a model has relatively little bias, it tends to provide accurate estimates of the quantities of interest Best model is often the simplest (less parameters)- model parsimony

301 views • 8 slides

6.1.4 AIC, Model Selection, and the Correct Model

6.1.4 AIC, Model Selection, and the Correct Model. Any model is a simplification of reality If a model has relatively little bias, it tends to provide accurate estimates of the quantities of interest Best model is often the simplest (less parameters)- model parsimony

448 views • 8 slides

Change Management

Change Management. Lecture 5 Diagnosis for change. Diagnostic tools. The image of the change manager impacts the types of tools that may be used.

967 views • 6 slides