'Model complexity' presentation slideshows

Hidden Markov models in Computational Biology

Hidden Markov models in Computational Biology. DTC Gerton Lunter, WTCHG February 2011 Includes material from: Dirk Husmeier , Heng Li. Overview. First part: Mathematical context: Bayesian Networks Markov models Hidden Markov models Second part:

1.03k views • 76 slides

Testing hypotheses using model selection

Testing hypotheses using model selection. Eric D. Stolen InoMedic Health Applications, Ecological Program, Kennedy Space Center, Florida NASA Environmental Management Branch. We h ve inv st d a l t of t m nd eff rt in cr at ng R, pl s c te it wh n us ng it f r d t n lys s. .

923 views • 74 slides

763 views • 56 slides

Introduction to Machine Learning

623 views • 48 slides

Bayesian Inference

Bayesian Inference. The Reverend Thomas Bayes (1702-1761). Sudhir Shankar Raman Translational Neuromodeling Unit, UZH & ETH. With many thanks for materials to: Klaas Enno Stephan & Kay H. Brodersen. Why do I need to learn about Bayesian stats?.

1.1k views • 48 slides

Regression Analysis

Summer Course: Data Mining. Regression Analysis. Presenter: Georgi Nalbantov. August 2009. Structure. Regression analysis: definition and examples Classical Linear Regression LASSO and Ridge Regression (linear and nonlinear)

646 views • 34 slides

Model Assessment and Selection

Model Assessment and Selection. Chapter 7 Neil Weisenfeld, et al. Introduction. Generalization performance of a learning method: Measure of prediction capability on independent test data Guide model selection Give quantitative assessment of chosen model

1k views • 32 slides

Computer Vision CS 776 Spring 2014

Computer Vision CS 776 Spring 2014. Recognition Machine Learning Prof. Alex Berg. Recognition: A machine learning approach. Slides adapted from Svetlana Lazebnik , Fei -Fei Li, Alex Berg, Rob Fergus, Antonio Torralba , Kristen Grauman , and Derek Hoiem.

387 views • 26 slides

ING models: how they work and how they are constructed

ING models: how they work and how they are constructed. I ndividual based N eural network G enetic algoritm by Espen Strand and Geir Huse. ING models - Presentation layout:. Representation of individuals Attribute and strategy vector, super-individual The genetic algorithm in ING models

367 views • 26 slides

Part 10: Decision Trees

Part 10: Decision Trees. Introduction Training Complexity, Pruning CART vs. ID3 vs. C4.5. Some materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000, Chapter 8.1-8.4 . Introduction.

436 views • 25 slides

Recognition: A machine learning approach

434 views • 24 slides

Manish Verma and Mark Friedl ,

Within and Across-Biome Seasonal Variation in GPP: An Assessment of Remote Sensing Methods Using the La Thuile Dataset. Manish Verma and Mark Friedl , Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA. Andrew D. Richardson

344 views • 22 slides

Data Mining / KDD

325 views • 22 slides

Data Mining

273 views • 19 slides

Sample Data

Road Map of Uncertainty. Sample Data. Spatial Precision Spatial Accuracy Sample Bias Identification Errors Date problems Gross Errors Gridding. Predictor Layers. Noise Correlation Interpolation Error Spatial Errors Measurement Errors Temporal Uncertainty. Modeling Software.

254 views • 14 slides

LECTURE 15: SUPPORT VECTOR MACHINES

LECTURE 15: SUPPORT VECTOR MACHINES. Objectives: Empirical Risk Minimization Large-Margin Classifiers Soft Margin Classifiers SVM Training Relevance Vector Machines Resources: DML: Introduction to SVMs AM: SVM Tutorial JP: SVM Resources OC: Taxonomy NC: SVM Tutorial. Class 2. Class 1.

688 views • 12 slides

WP3: Qualitative Fault Modelling

183 views • 11 slides

Using uncertainty to test model complexity

Using uncertainty to test model complexity . Barry Croke. Concept . Possible to test whether a model has an appropriate complexity through analysis of scatter in residuals Take the standard deviation of the uncertainty distribution as a measure of uncertainty

167 views • 5 slides

Svetha Venkatesh Curtin University, Australia

179 views • 4 slides

A penalty term against model complexity where K is the number of parameters for a given model

State prior. State transition. Conditional observation. A penalty term against model complexity where K is the number of parameters for a given model. Likelihood for fitting a given model to the observed event classes at each time frame over time T.

155 views • 1 slides