'Gaussian distribution' diaporamas de présentation

PDE methods for Image Segmentation and Shape Analysis:

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WiFi and WCDMA Network Design

WiFi and WCDMA Network Design. Robert Akl, D.Sc. Department of Computer Science and Engineering. Outline. WiFi Access point selection Traffic balancing Multi-Cell WCDMA with Multiple Classes User modeling using 2D Gaussian distribution Intra-cell and inter-cell interference and capacity .

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New Segmentation Technique

New Segmentation Technique. Speaker: Yu-Hsiang Wang Advisor: Prof. Jian -Jung Ding Digital Image and Signal Processing Lab Graduate Institute of Communication Engineering National Taiwan University. Outline . Introduction JSEG Criterion for Segmentation Seed Determination

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On the Statistical Analysis of Dirty Pictures

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Robust Bayesian Portfolio Construction

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A note about gradient descent: Consider the function f(x)=(x-x 0 ) 2 Its derivative is: By gradient descent

A note about gradient descent: Consider the function f(x)=(x-x 0 ) 2 Its derivative is: By gradient descent (If f(x) is more complex we usually cannot solve explicitly the convergence to the fixed point s .). + -. x 0. Solving the differential equation:.

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Quantitative Genetics

Quantitative Genetics. Quantitative Traits. Mendel worked with traits that were all discrete, either/or traits: yellow or green, round or wrinkled, etc. Different alleles gave clearly distinguishable phenotypes.

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Quantitative Genetics

Quantitative Genetics. Quantitative Traits. Mendel worked with traits that were all discrete, either/or traits: yellow or green, round or wrinkled, etc. Different alleles gave clearly distinguishable phenotypes.

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Quantitative Genetics

Quantitative Genetics. Quantitative Traits. Mendel worked with traits that were all discrete, either/or traits: yellow or green, round or wrinkled, etc. Different alleles gave clearly distinguishable phenotypes.

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Automatic Speech Recognition: Conditional Random Fields for ASR

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Probabilistic Reasoning for Modeling Unreliable Data

Probabilistic Reasoning for Modeling Unreliable Data . Ron Tal York University. Agenda. Modeling Uncertainty Bayesian Reasoning M-Estimation Maximum Likelihood Common Pitfall More Advanced Models . Modeling Uncertainty. Why is it necessary?

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Estimation of the Primal Production Function

Estimation of the Primal Production Function. Lecture V. Ordinary Least Squares. The most straightforward concept in the estimation of production function is the application of ordinary least squares.

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Gaussian Processes

Gaussian Processes. Li An anli@temple.edu. The Plan. Introduction to Gaussian Processes Revisit Linear regression Linear regression updated by Gaussian Processes Gaussian Processes for Regression Conclusion. Why GPs?. Here are some data points! What function did they come from?

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Types of Errors

Types of Errors. Difference between measured result and true value. Illegitimate errors Blunders resulting from mistakes in procedure. You must be careful. Computational or calculational errors after the experiment. Bias or Systematic errors

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Analog Devices ICs for In Vitro Diagnostics Equipment

Analog Devices ICs for In Vitro Diagnostics Equipment. Agenda. In Vitro Diagnostics Overview Example Application: Flow Cytometry General block diagram Detectors & Sensors Amplifying signals from photodiodes Digitizing the analog signals Digital processing

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Chapter 6. Classification and Prediction

Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation Support Vector Machines (SVM) Associative classification. Chapter 6. Classification and Prediction. Bayesian Classification.

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Carl Friedrich Gauss

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