'Cost function' diaporamas de présentation

Cost Minimization and Cost Curves

Cost Minimization and Cost Curves. Beattie, Taylor, and Watts Sections: 3.1a, 3.2a-b, 4.1. Agenda. The Cost Function and General Cost Minimization Cost Minimization with One Variable Input Deriving the Average Cost and Marginal Cost for One Input and One Output

735 views • 46 slides

On the ICP Algorithm

929 views • 38 slides

The Flypaper Effect

The Flypaper Effect. Phuong Nguyen-Hoang, University of Iowa John Yinger, Syracuse University April 2019. The Flypaper Effect. Outline

633 views • 37 slides

Probabilistic tomography

Probabilistic tomography. Jeannot Trampert Utrecht University. Is this the only model compatible with the data?. Project some chosen model onto the model null space of the forward operator. Then add to original model. The same data fit is guaranteed!. Deal et al. JGR 1999. True model.

499 views • 37 slides

TuneIn: How to get your jobs tuned while sleeping

729 views • 36 slides

An OSSE-based evaluation of 4D- Ensemble-Var (and hybrid variants) for the NCEP GFS

An OSSE-based evaluation of 4D- Ensemble-Var (and hybrid variants) for the NCEP GFS. 1 NOAA/NWS/NCEP/EMC 2 Univ. of Maryland-College Park, Dept. of Atmos. & Oceanic Science. Daryl Kleist 1,2 and Kayo Ide 2. w ith acknowledgements to Dave Parrish, Jeff Whitaker, John Derber , Russ Treadon.

747 views • 35 slides

3.7 Rates of Change in the Natural and Social Sciences

DIFFERENTIATION RULES. 3.7 Rates of Change in the Natural and Social Sciences. In this section, we will examine: Some applications of the rate of change to physics, chemistry, biology, economics, and other sciences. PHYSICS.

1.04k views • 35 slides

Characterization of Aura-TES (Tropospheric Emission Spectrometer) Nadir and Limb Retrievals

Characterization of Aura-TES (Tropospheric Emission Spectrometer) Nadir and Limb Retrievals. Helen Worden, Reinhard Beer, Kevin Bowman, Annmarie Eldering, Mingzhao Luo, Gregory Osterman, Susan Sund Kulawik, John Worden Jet Propulsion Laboratory, California Institute of Technology

448 views • 32 slides

Time-series data analysis

Time-series data analysis. Why deal with sequential data?. Because all data is sequential ? All data items arrive in the data store in some order Examples transaction data documents and words In some (or many) cases the order does not matter In many cases the order is of interest.

404 views • 28 slides

Correlation Aware Feature Selection

374 views • 24 slides

Linear Regression

547 views • 23 slides

Adaptive Wave Field Synthesis for Surround Sound Reproduction from an Array of Loudspeakers

Adaptive Wave Field Synthesis for Surround Sound Reproduction from an Array of Loudspeakers . ECE 463: Adaptive Filters Project Presentation: March 9, 2006 Louis Terry. Presentation Overview. Motivation Problem Statement Generalized Problem Statement Mathematical Background

531 views • 22 slides

Recursive Least-Squares (RLS) Adaptive Filters

Recursive Least-Squares (RLS) Adaptive Filters. Definition. With the arrival of new data samples estimates are updated recursively. Introduce a weighting factor to the sum-of-error-squares definition. two time-indices n: outer, i: inner. Weighting factor. Forgetting factor.

3.74k views • 21 slides

Machine Learning Artificial Neural Networks

560 views • 19 slides

Threshold Voltage Assignment to Supply Voltage Islands in Core-based System-on-a-Chip Designs

Threshold Voltage Assignment to Supply Voltage Islands in Core-based System-on-a-Chip Designs. Milestone 2: Steven Beigelmacher Gall Gotfried www.ece.cmu.edu/~ggall 03/23/2005. Overview. Review What are we doing? How are we doing it? Parquet Code overview

195 views • 18 slides

Learning from Demonstrations

Learning from Demonstrations. Jur van den Berg. Kalman Filtering and Smoothing. Dynamics and Observation model Kalman Filter: Compute Real-time, given data so far Kalman Smoother: Compute Post-processing, given all data. EM Algorithm. Kalman smoother:

289 views • 16 slides

Matematika ekonomi

learning subject

1.6k views • 14 slides

Image Denoising using Wavelet Thresholding Techniques

Image Denoising using Wavelet Thresholding Techniques. Submitted by Yang Yang 9024553282. Introduction. Image denoising: Removing unwanted noise in order to restore the original image. Wavelet transform provides us with one of the methods for image denoising.

929 views • 13 slides

Some Additional Cost Concepts

Some Additional Cost Concepts. The author suggests that a good function to represent total cost has the general form TC = f + aQ + bQ^2 + cQ^3, Where the key ^ means raise to power, f represents fixed cost, Q represents the level of output that can vary, and

309 views • 7 slides

$350,110,000 $351,110,000 $350,410,000 $351,310,000 $350,810,000 $351,110,000

122 views • 5 slides