'Loss function' presentation slideshows

Collaborative Filtering

Collaborative Filtering Rong Jin Dept. of Computer Science and Engineering Michigan State University Information Filtering Basic filtering question: Will user U like item X ? Two different ways of answering it Look at what U likes ? characterize X ? content-based filtering

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A Robust Real Time Face Detection

919 views • 53 slides

Chapter 5, Part 1

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Object recognition

Object recognition. Methods for classification and image representation. Credits. Slides by Pete Barnum Slides by Fei-Fei Li Paul Viola, Michael Jones, Robust Real-time Object Detection, IJCV 04 Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

679 views • 42 slides

Support Vector Machine

Support Vector Machine. Chan-Gu Gang, MK Hasan and Ha-Yong, Jung 2004/11/17. Introduction. Learning Theory. Objective: Two classes of objects ? new object ? assign it to one of the two classes Binary Pattern Recognition (Binary Classification) xi : pattern, case, input, instance, ..

781 views • 42 slides

Dealing with double default under Basel II

673 views • 40 slides

Supervised Learning Recap

Supervised Learning Recap. Machine Learning. Last Time. Support Vector Machines Kernel Methods. Today. Review of Supervised Learning Unsupervised Learning ( Soft) K-means clustering Expectation Maximization Spectral Clustering Principle Components Analysis Latent Semantic Analysis.

638 views • 40 slides

Support Vector Machines: Hype or Hallelujah?

Support Vector Machines: Hype or Hallelujah?. Kristin Bennett Math Sciences Dept Rensselaer Polytechnic Inst. http://www.rpi.edu/~bennek. Outline. Support Vector Machines for Classification Linear Discrimination Nonlinear Discrimination Extensions Application in Drug Design Hallelujah

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Model Assessment, Selection and Averaging

Model Assessment, Selection and Averaging. Presented by: Bibhas Chakraborty . Performance Assessment: Loss Function. Typical choices for quantitative response Y : (squared error)

578 views • 35 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

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Presented by Jian Chen PhD (Applied Statistics) MS (Computer Science) Sr. Statistician, Credigy

Statistical computing with SAS/IML. Presented by Jian Chen PhD (Applied Statistics) MS (Computer Science) Sr. Statistician, Credigy . SAS/IML. SAS Interactive Matrix Language: Beyond!. Outline. Overview of SAS/IML. Language nuts and bolts. An example in Bayesian Analysis.

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Conditional Random Fields

Conditional Random Fields. Jie Tang KEG, DCST, Tsinghua 24, Nov, 2005. Sequence Labeling. Pos Tagging E.g. [He/PRP] [reckons/VBZ] [the/DT] [current/JJ] [account/NN] [deficit/NN] [will/MD] [narrow/VB] [to/TO] [only/RB] [#/#] [1.8/CD] [billion/CD] [in/IN] [September/NNP] [./.] Term Extraction

429 views • 26 slides

Antin Korotkov OISM 470 Section 4 02/14/02

Antin Korotkov OISM 470 Section 4 02/14/02. Taguchi & Quality Loss Function. Topics Covered. Dr. Genichi Taguchi The Taguchi Method - Quality Loss Function. Dr. Genichi Taguchi. Widely acknowledged leader in the U.S. industrial quality movement.

454 views • 24 slides

Probabilistic Re-Analysis Using Monte Carlo Simulation

Probabilistic Re-Analysis Using Monte Carlo Simulation. Efstratios Nikolaidis, Sirine Salem, Farizal, Zissimos Mourelatos April 2008. Definition and Significance. Probabilistic design optimization Find design variables

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SVM Support Vectors Machines

SVM Support Vectors Machines. Based on Statistical Learning Theory of Vapnik, Chervonenkis, Burges, Scholkopf, Smola, Bartlett, Mendelson, Cristianini Presented By: Tamer Salman. The addressed Problems. SVM can deal with three kinds of problems: Pattern Recognition / Classification.

405 views • 22 slides

2. Bayes Decision Theory

2. Bayes Decision Theory. Prof. A.L. Yuille Stat 231. Fall 2004. Decisions with Uncertainty. Bayes Decision Theory is a theory for how to make decisions in the presence of uncertainty. Input data x. Salmon y= +1, Sea Bass y=-1. Learn decision rule: f(x) taking values .

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CHAPTER 10 E VOLUTIONARY C OMPUTATION II : G ENERAL M ETHODS AND T HEORY

Slides for Introduction to Stochastic Search and Optimization ( ISSO ) by J. C. Spall. CHAPTER 10 E VOLUTIONARY C OMPUTATION II : G ENERAL M ETHODS AND T HEORY. Organization of chapter in ISSO Introduction Evolution strategy and evolutionary programming; comparisons with GAs

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CHAPTER 10 E VOLUTIONARY C OMPUTATION II : G ENERAL M ETHODS AND T HEORY

Slides for Introduction to Stochastic Search and Optimization ( ISSO ) by J. C. Spall. CHAPTER 10 E VOLUTIONARY C OMPUTATION II : G ENERAL M ETHODS AND T HEORY. Organization of chapter in ISSO Introduction Evolution strategy and evolutionary programming; comparisons with GAs

433 views • 20 slides

Hidden Markov Models & Conditional Random Fields

Hidden Markov Models & Conditional Random Fields. Eran Segal Weizmann Institute. Representing Time. Add the time dimension to variables X ? X (t) Assumptions Time can be discretized into interesting points t 1 ,t 2 ,...t n Markov assumption holds

929 views • 16 slides

Chapter 2

Chapter 2. Basic Considerations for Successful Forecasting. Six Considerations. Decision Environment - Loss Function Forecast Objective Forecast Statement Forecast Horizon Information Set Complexity of Methodology. Decision Environment.

197 views • 11 slides