We describe a set of supervised machine learning experiments centering on the construction of statistical models of WH-questions. These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user’s informational goals. We report on different aspects of the predictive performance of our models, including the inﬂuence of various training and testing factors on predictive performance, and examine the relationships among the target variables. ...
This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling document-word cooccurrences, are presented and evaluated on datasets of human plausibility judgements. Compared to previously proposed techniques, these models perform very competitively, especially for infrequent predicate-argument combinations where they exceed the quality of Web-scale predictions while using relatively little data. ...
Variable stars are those that change brightness. Their variability may be due to geometric processes such as rotation, or eclipse by a companion star, or physical processes such as vibration, ﬂares, or cataclysmic explosions. In each case, variable stars provide unique information about the properties of stars, and the processes that go on within them.
Chapter 9(1) introduce to tssertions, wiews and programming techniques. The main contents in this chapter: Constraints as assertions, SQL triggers, views (virtual tables) in SQL, programming techniques, variables, execution of the SQL statement,...
Chapter 1 - What is statistics? When you have completed this chapter, you will be able to: Explain what is meant by statistics, identify the role of statistics in the development of knowledge and everyday life, explain what is meant by descriptive statistics and inferential statistics, distinguish between a qualitative variable and a quantitative variable,...
When you have completed this chapter, you will be able to: Define the terms probability distribution and random variable; distinguish between discrete and continuous random variables; calculate the mean, variance, and standard deviation of a discrete probability distribution; describe the characteristics and compute probabilities using the Poisson probability distribution.
When you have completed this chapter, you will be able to: Explain how probabilities are assigned to a continuous random variable, explain the characteristics of a normal probability distribution, define and calculate z value corresponding to any observation on a normal distribution, determine the probability a random observation is in a given interval on a normal distribution using the standard normal distribution, use the normal probability distribution to approximate the binomial probability distribution.
Chapter 13 - Linear regression and correlation, after studying this chapter you will be able to: Identify a relationship between variables on a scatter diagram, measure and interpret a degree of relationship by a coefficient of correlation, conduct a test of hypothesis about the coefficient of correlation in a population,...and other contents.
This text is based on a lecture course developed by the author and given to students in the second year of study in mathematics at Newcastle University. This has been written to provide a typical course (for students with a general mathematical background) that introduces the main ideas, concepts and techniques, rather than a wide-ranging and more general text on complex analysis.
Chapter 1 - What is statistics? When you have completed this chapter, you will be able to: Understand why we study statistics, explain what is meant by descriptive statistics and inferential statistics, distinguish between a qualitative variable and a quantitative variable, describe how a discrete variable is different from a continuous variable, distinguish among the nominal, ordinal, interval, and ratio levels of measurement.
Chapter 6 - Discrete probability distributions. In this chapter, the learning objectives are: Define the terms probability distribution and random variable; distinguish between discrete and continuous probability distributions; calculate the mean, variance, and standard deviation of a discrete probability distribution;...
Chapter 13 - Linear regression and correlation. When you have completed this chapter, you will be able to: Understand and interpret the terms dependent and independent variable; calculate and interpret the coefficient of correlation, the coefficient of determination, and the standard error of estimate; conduct a test of hypothesis to determine whether the coefficient of correlation in the population is zero;...
Chapter 14 - Multiple regressions and correlation analysis. In this chapter, the learning objectives are: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis; set up, interpret, and apply an ANOVA table compute and interpret the multiple standard error of estimate, the coefficient of multiple determination, and the adjusted coefficient of multiple determination; conduct a test of hypothesis to determine whether regression coefficients differ from zero;...
Chapter 19 - Statistical process control and quality management. In this chapter, the learning objectives are: Discuss the role of quality control in production and service operations; define and understand the terms chance cause, assignable cause, in control, out of control, attribute, and variable; construct and interpret a pareto chart; construct and interpret a fishbone diagram; construct and interpret mean and range charts;...
Most modern programming languages provide support for some type of object that can hold
a variable number of elements. These objects are referred to as collections, and they can have
elements added and removed with ease without having to worry about proper memory allocation.
If you’ve programmed with classic ASP before, you’re probably familiar with the
Scripting.Dictionary object, a collection object that references each element with a
textual key. A collection that stores objects in this fashion is known as a hash table.
Update to Wrox′s leading C# book for beginners Get ready for the next release of Microsoft′s C# programming language with this essential Wrox beginner′s guide. Beginning Microsoft Visual C# 2010 starts with the basics and brings you thoroughly up to speed. You′ll first cover the fundamentals such as variables, flow control, and object–oriented programming and gradually build your skills for Web and Windows programming,
Thanks to a long, successful marketing campaign by De Beers, diamonds became strongly associated with
romantic love, first in the United States and then globally (see.Figure.5). In the 1940s the company launched
a long-running and renowned campaign around the theme “A diamond is forever.” Over many decades,
hundreds of millions of dollars were spent to market the notion that diamonds signify romance and love.
That campaign benefited the entire diamond industry.
Applied statistics for civil and environmental engineers has many contents: Preliminary Data Analysis, Basic Probability Concepts, Random Variables and Their Properties, Model Estimation and Testing, Methods of Regression and Multivariate Analysis, Frequency Analysis of Extreme Events, Simulation Techniques for Design, Risk and Reliability Analysis, Bayesian Decision Methods and Parameter Uncertainty.
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The book is divided into two parts: Circuit Design and System Design. The first part
deals with everything that goes directly inside the main code, while the second deals
with units that might be located in a library (for code sharing, reuse, and partitioning).