Results 271 to 280 of about 921,406 (312)

A non-parametric topical relevance model

Information Retrieval Journal, 2018
An information retrieval (IR) system can often fail to retrieve relevant documents due to the incomplete specification of information need in the user’s query. Pseudo-relevance feedback (PRF) aims to improve IR effectiveness by exploiting potentially relevant aspects of the information need present in the documents retrieved in an initial search ...
Debasis Ganguly, Gareth J. F. Jones
openaire   +1 more source

A Non-Parametric Approach to Model Evaluation

Journal of the Operational Research Society, 1986
All existing tests and criteria for model evaluation are based on the sum of squared residuals. These measures do not evaluate the performance of a model in predicting turning points in the variable of interest. Here, three tests are proposed to evaluate models on the basis of their ability to predict turning points and to give an unbiased prediction ...
Dadkhah, Kamran M., Zahedi, Fatemeh
openaire   +2 more sources

Learning non-parametric models of pronunciation

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
As more data becomes available for a given speech recognition task, the natural way to improve recognition accuracy is to train larger models. But, while this strategy yields modest improvements to small systems, the relative gains diminish as the data and models grow.
Brian Hutchinson, Jasha Droppo
openaire   +1 more source

A Bayesian non-parametric stochastic frontier model

Annals of Tourism Research, 2021
In this paper, we introduce a new Bayesian non-parametric stochastic frontier (SF) model that addresses the endogeneity problem and relaxes problematic assumptions regarding functional form, and distributional properties. The model can be seen as a competitor to DEA.
Assaf, A. George, Tsionas, Mike, Kock, Florian, Josiassen, Alexander   +3 more
openaire   +3 more sources

Non-parametric identification of generalized Hammerstein models

International Journal of Systems Science, 1998
The Hammerstein model is considered in a generalized form, where its nonlinear element can have multi-inputs and a finite memory. The identification of the multi-input finite memory nonimearity and the impulse response sequence of the model is treated using a non-parametric approach. A numerical example is given.
Hosam E. Emara-Shabaik, Kamal A. F. Moustafa   +1 more
openaire   +1 more source

Non-parametric Model for Background Subtraction

2000
Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel non-parametric background model and a background subtraction approach. The model can handle situations where the background of the scene is cluttered
Ahmed M. Elgammal, David Harwood, Larry S. Davis   +2 more
openaire   +1 more source

A non-parametric approach to behavioral device modeling

2010 11th International Symposium on Quality Electronic Design (ISQED), 2010
This work proposes a non-parametric methodology for quick and effective behavioral macromodeling of complex digital and analog devices. Gaussian Process Regression (GPR) learning algorithms are used to generate simple, robust, and widely applicable time-domain models without specifying device equations or parameters.
Dragoljub Gagi Drmanac, Brendon Bolin, Li-C. Wang   +2 more
openaire   +1 more source

Non-parametric Spectral Model for Shape Retrieval

2015 International Conference on 3D Vision, 2015
Non-rigid 3D shape retrieval is an active and important research topic in content based object retrieval. This problem is often cast in terms of the shapes intrinsic geometry due to its invariance to a wide range of non-rigid deformations. In this paper, we devise a novel generative model for shape retrieval based on the spectral representation of the ...
Andrea Gasparetto, Giorgia Minello, Andrea Torsello   +2 more
openaire   +1 more source

Simulated Non-Parametric Estimation of Dynamic Models

Review of Economic Studies, 2009
Summary: This paper introduces a new class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint) densities estimated from data simulated out of the ...
Filippo Altissimo, Antonio Mele
openaire   +3 more sources