'Error function' presentation slideshows

Long-term image-based motion estimation

Long-term image-based motion estimation. Dennis Strelow and Sanjiv Singh. On the web. Related materials: these slides related papers movies VRML models at: http://www.cs.cmu.edu/~dstrelow/maryland. Introduction (1). micro air vehicle (MAV) navigation. AeroVironment Black Widow.

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Motion from normal flow

Motion from normal flow . The aperture problem. Depth discontinuities. Optical flow difficulties. Translational Normal Flow. In the case of translation each normal flow vector constrains the location of the FOE to a half-plane. Intersection of half-planes provides FOE.

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PATTERN RECOGNITION AND MACHINE LEARNING

PATTERN RECOGNITION AND MACHINE LEARNING. CHAPTER 3: LINEAR MODELS FOR REGRESSION. Outline. Discuss tutorial. Regression Examples. The Gaussian distribution. Linear Regression. Maximum Likelihood estimation. Polynomial Curve Fitting . Academia Example.

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Introduction to Machine Learning

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Fuzzy Support Vector Machines (FSVM s )

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DCSP-5: Noise

DCSP-5: Noise. Jianfeng Feng Department of Computer Science Warwick Univ., UK Jianfeng.feng@warwick.ac.uk http://www.dcs.warwick.ac.uk/~feng/dsp.html. Assignment. Question 8. load handel for i=1:73113 noise(i)=cos(100*i*0.1); end x=y+noise'; sound(x).

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Breakdown turn-on time from TBTS and KEK

Breakdown turn-on time from TBTS and KEK. Alexey Dubrovskiy. Examples#1 of RF breakdowns. M. E. M. 80 ns. 80 ns. 80 ns. 80 ns. 80 ns. 80 ns. 80 ns. 80 ns. 80 ns. M. B. B. B. B. E. a t the middle. E. M. B. BD at the end of the ACS. at the beginning.

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Some Extensions to the Integral Equation Method for Electromagnetic Scattering from Rough Surfaces

Some Extensions to the Integral Equation Method for Electromagnetic Scattering from Rough Surfaces. Yang Du zjuydu03@zju.edu.cn. Zhejiang University, Hangzhou, China. 2011.7.25. Outline. The analytical models. conventional and the unifying models Recent advances

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Biological and Artificial Neuron

Neural Networks. Learning Processes. Biological and Artificial Neuron. Weights, need to be determined. Biological neuron. Bias, need to be determined. Artificial neuron. Neural Networks. Learning Processes. Application of Neural Networks. Function approximation and prediction

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Simultaneous Perturbation Learning Rule for Recurrent Neural Networks and Its FPGA Implementation

?? ?????? National Taiwan Ocean University. Simultaneous Perturbation Learning Rule for Recurrent Neural Networks and Its FPGA Implementation. ????????? Department of Communication, Navigation and Control Engineering. ?? : ??? ??????. References.

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Mass Transport of Pollutants

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Hadoop

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Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 7: Learning in recurrent networks. Geoffrey Hinton. Recurrent networks. A feed-forward neural net just computes a fixed sequence of non-linear learned transformations to convert an input patter into an output pattern.

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Neural Networks: Hessians

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Neural Networks and SVM

Neural Networks and SVM. Stat 600. Neural Networks. History: started in the 50s and peaked in the 90s Idea: learning the way the brain does. Numerous applications Handwriting, face, speech recognition Vehicles that drive themselves Models of reading, sentence production, dreaming.

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A low-power efficient direct digital frequency synthesizer based on new two-level lookup table

A low-power efficient direct digital frequency synthesizer based on new two-level lookup table. Presenter ? Cheng-Hsun Liang Number:97662003 1 1. Kun-Tse Lee, Jin-Jia Chen Department of Electrical Engineering, NCUE Changhua, Taiwan 500, ROC. Outline. INTRODUCTION

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LondonR 18 th September 2012

LondonR 18 th September 2012. SPONSORED BY. R TRAINING, CONSULTING & APPLICATION DEVELOPMENT. From Back-test to Trade using R Parallelization in OneTick. jjames@mango-solutions.com andrew.diamond@onetick.com. What the presentation shows. EURJPY FX tick data

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SVM for Regression

SVM for Regression. DMML Lab 04/20/07. SVM Recall. Two-class classification problem using linear model:. In linear regression, we minimize the error function:. Regularized Error Function. Replace the quadratic error function by ? -insensitive error function :.

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Fuzzy Logic Control of HVAC Systems

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