Gaussian Mixture Models∗ Douglas Reynolds MIT Lincoln Laboratory‚ 244 Wood St.‚ Lexington‚ MA 02140‚ USA dar@ll.mit.edu Synonyms GMM; Mixture model; Gaussian mixture density Definition A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system‚ such as vocal-tract related
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Create a plan for the collection of primary and secondary data for a given business problem-p1 Data is a plural of datum‚ which is originally a Latin noun meaning "something given". Today‚ data is used in English both as a plural noun meaning "facts or pieces of information". Data is simply a "scientific" term for facts‚ figures‚ information and measurements. Example; People with white hair. Data collection by an organisation can be internal or external. Internal data- Information‚ facts
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function varargout = exudates(varargin) gui_Singleton = 1; gui_State = struct(’gui_Name’‚ mfilename‚ ... ’gui_Singleton’‚ gui_Singleton‚ ... ’gui_OpeningFcn’‚ @exudates_OpeningFcn‚ ... ’gui_OutputFcn’‚ @exudates_OutputFcn‚ ... ’gui_LayoutFcn’‚ [] ‚ ... ’gui_Callback’‚ []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout
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INTRODUCTION 1.1 Aim of the Thesis This project presents an interactive algorithm to automatically segment out a person’s face from a given image that consists of a head-and-shoulders view of the person and a complex background scene. The method involves a fast‚ reliable‚ and effective algorithm that exploits the spatial distribution characteristics of human skin color. To fulfill this aim‚ the following objectives are carried out: 1. Implement different types of image segmentations. 2. A
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The total sample proportion for each color was blue .207‚ orange - .2298‚ green -.176‚ yellow - .1376‚ red - .1268‚ and brown - .1229. In addition to totaling the candies and calculating the sample proportion of each color‚ a histogram was created. The histogram is a graphical representation of the probability distribution of a given variable depicting the
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aspects of a ratio (continuous) data type. The important statistics that will be discussed whenever there is an analysis as such will be highlighted in Black. However‚ as done before the frequency table is provided with a graphical representation (Histogram) which displays the data. Now specifically referring to the data analysis there are specific statistics that shall be discussed and explained as follows: * Mean: which provides the centrally tendency of this sample data‚ and concludes that
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sakifkhan@gmail.com PUSAT PEMBELAJARAN : MELAKA CONTENT 1.0 QUESTION 1 Q1 (a) Construct a cumulative frequency distribution Q1 (b) Draw Cumulative Frequency Polygon Q1 (c ) Q1 (d ) 2.0 QUESTION 2 Q2 (a ) Type of variable Q2 (b) Histogram Q2 (c ) Q2 (d) Q2 (e ) mean ‚ mode ‚ median Question 1 a ) Construct a cumulative frequency distribution Number of Cars | Frequency | Upper Boundary | Cumulative frequency | 15-19 | 9 | ≤19.5 | 9 | 20-24 | 8 | ≤24.5 | 17 |
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CAVITE NATIONAL SCIENCE HIGH SCHOOL Maragondon‚ Cavite Using a Digital Camera to Measure Light Pollution of Towns in Cavite Prepared by Price Brix Rexson Q. Bernal IV-Hydrogen INTRODUCTION Light pollution‚ also known as photopollution or luminous pollution‚ is excessive or obtrusive artificial light. * The International Dark-Sky Association (IDA) defines light pollution as: Any adverse effect of artificial light including sky glow‚ glare‚ light trespass‚ light clutter‚ decreased
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Question 1. (Descriptive Statistics) Investment Returns: These data are the annual returns on shareholders’ funds of 97 of Australian’s top 100 companies for the years 1990 and 1998. (i) Produce a histogram of the 1990 returns. (ii) Produce a histogram of the 1998 returns. (iii) Find the mean‚ median‚ range and standard deviation for the 1990 returns. Annual Returns % (1990) Mean 12.91865979 Median 11.38 Standard Deviation 9.297513067 Range 75.01 (iv) Repeat part (iii) for
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Abstract Recent investigations have shown that exerting self control reduces the ability to regulate behaviour in subsequent self control tasks. A state of depleted self control resources is known as ‘ego depletion’ and the purpose of this study was to see how our capacity to inhibit urges can be affected by ego depletion. This study was also done to test the effects of beliefs about self control on subsequent performance in self control tasks. Participants’ performance on the Stroop task and
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