eLearning

Dialect/Accent Classification Using Unrestricted Audio

Rongqing Huang; John H. L. Hansen; Pongtep Angkititrakul IEEE Transactions on Audio, Speech, and Language Processing, 2007

This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the ...

Agriculture yield prediction using predictive analytic techniques

S. Nagini; T. V. Rajini Kanth; B. V. Kiranmayee 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 2016

India's economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, ...

Bounds on performance for multiple target tracking

F. E. Daum IEEE Transactions on Automatic Control, 1990

A theoretical lower bound on mean-square-estimation error is derived for tracking in dense multiple-target environments. This family of bounds is computationally tractable, because it does not require computing the optimal estimate. Computational complexity can be traded for tightness of the lower bounds by varying the number of hypotheses considered. The theory can be used to study the fundamental limitations of ...

Robust layered sensing: From sparse signals to sparse residuals

Vassilis Kekatos; Georgios B. Giannakis 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010

One of the key challenges in sensing networks is the extraction of information by fusing data from a multitude of possibly unreliable sensors. Robust sensing, viewed here as the simultaneous recovery of the wanted information- bearing signal vector together with the subset of (un)reliable sensors, is a problem whose optimum solution incurs combinatorial complexity. The present paper relaxes this problem ...

Tracking algorithm designed by the local asymptotic approach

E. Wahnon; N. Berman IEEE Transactions on Automatic Control, 1990

The problem of sequential detection of parameter jumps in linear systems with constant noise level is discussed. The detection problem is analyzed by the asymptotic local approach, using the normalized output error sequence as the detection signal. For linear regression, ARMAX, and state-space models, a central limit theorem is proved, transforming the original problem into the problem of detecting an ...

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IEEE-USA E-Books

• Regression Estimation

  This chapter contains sections titled: Linear Regression with Insensitive Loss function, Dual Problems, -SV Regression, Convex Combinations and 1-Norms, Parametric Insensitivity Models, Applications, Summary, Problems

• No title

  There are many books written about statistics, some brief, some detailed, some humorous, some colorful, and some quite dry. Each of these texts is designed for a specific audience. Too often, texts about statistics have been rather theoretical and intimidating for those not practicing statistical analysis on a routine basis. Thus, many engineers and scientists, who need to use statistics much more frequently than calculus or differential equations, lack sufficient knowledge of the use of statistics. The audience that is addressed in this text is the university-level biomedical engineering student who needs a bare-bones coverage of the most basic statistical analysis frequently used in biomedical engineering practice. The text introduces students to the essential vocabulary and basic concepts of probability and statistics that are required to perform the numerical summary and statistical analysis used in the biomedical field. This text is considered a starting point for important issue to consider when designing experiments, summarizing data, assuming a probability model for the data, testing hypotheses, and drawing conclusions from sampled data. A student who has completed this text should have sufficient vocabulary to read more advanced texts on statistics and further their knowledge about additional numerical analyses that are used in the biomedical engineering field but are beyond the scope of this text. This book is designed to supplement an undergraduate-level course in applied statistics, specifically in biomedical engineering. Practicing engineers who have not had formal instruction in statistics may also use this text as a simple, brief introduction to statistics used in biomedical engineering. The emphasis is on the application of statistics, the assumptions made in applying the statistical tests, the limitations of these elementary statistical methods, and the errors often committed in using statistical analysis. A number of examples from biomedical engi eering research and industry practice are provided to assist the reader in understanding concepts and application. It is beneficial for the reader to have some background in the life sciences and physiology and to be familiar with basic biomedical instrumentation used in the clinical environment. Contents: Introduction / Collecting Data and Experimental Design / Data Summary and Descriptive Statistics / Assuming a Probability Model from the Sample Data / Statistical Inference / Linear Regression and Correlation Analysis / Power Analysis and Sample Size / Just the Beginning / Bibliography

• A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift

  This chapter contains sections titled: Conditional Expectation Analysis of Generalization Error, Linear Regression under Covariate Shift, Model Selection, Active Learning, Active Learning with Model Selection, Conclusions

• Generalization of Linear Regression Problems

  This chapter contains sections titled: Introduction Generalized Total Least Squares (GeTLS EXIN) Approach GeTLS Stability Analysis Neural Nongeneric Unidimensional TLS Scheduling Accelerated MCA EXIN Neuron (MCA EXIN+) Further Considerations Simulations for the GeTLS EXIN Neuron

• Complements and Details

  This chapter contains sections titled: Optimum Gains for Recursive Linear Regression, "Quick and Dirty" Recursive Linear Regression, Optimum Gains for Recursive Linear Regression. Batch Processing, "Quick and Dirty" Linear Regression. Batch Processing, Gain Sequences for Recursive Nonlinear Regression. The Method of Linearization, Sufficient Conditions for Assumptions E1 ThroughE E6′ (E6) When the Gains (Equations 7.48) Are Used, Limitations of the Recursive Method. III Conditioning, Response Surfaces

• Prediction in Probability Spaces

  This chapter contains sections titled: Conditional Distribution, Regression on a Single Variable, Regression on a Partition or a Family of Variables, Linear Regression on a Single Variable, Linear Regression on a Family of Variables

• Multiple Regression and Model Building

  Multiple regression, where more than one predictor variable is used to estimate a response variable, is introduced by way of an example. To allow for inference, the multiple regression model is defined, with both model and inferential methods representing extensions of the simple linear regression case. Next, regression with categorical predictors (indicator variables) is explained. The problems of multicollinearity are examined; multicollinearity represents an unstable response surface due to overly correlated predictors. The variance inflation factor is defined, as an aid in identifying multicollinear predictors. Variable selection methods are then provided, including forward selection, backward elimination, stepwise, and best-subsets regression. Mallows'C p statistic is defined, as an aid in variable selection. Finally, methods for using the principal components as predictors in multiple regression are discussed.

• Joint Kernel Maps

  This chapter contains sections titled: Introduction, Incorporating Correlations into Linear Regression, Linear Maps and Kernel Methods : Generalizing Support Vector Machines, Joint Kernel Maps, Joint Kernel, Experiments, Conclusions

• Linear Regression

  This chapter contains sections titled: 12.1 Introduction, 12.2 Least-Squares Estimation, 12.3 The Linear Model, 12.4 Universal Models for Linear Regression

• High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression

  We focus on the problem of estimating the graph structure associated with a discrete Markov random field. We describe a method based on ℓ1-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an ℓ1-constraint. Our framework applies to the high-dimensional setting, in which both the number of nodes p and maximum neighborhood sizes d are allowed to grow as a function of the number of observations n. Our main result is to establish sufficient conditions on the triple (n, p, d) for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously. Under certain mutual incoherence conditions analogous to those imposed in previous work on linear regression, we prove that consistent neighborhood selection can be obtained as long as the number of observations n grows more quickly than 6d6 log d + 2d5 log p, thereby establishing that logarithmic growth in the number of samples n relative to graph size p is sufficient to achieve neighborhood consistency.