Structured Regression on Multiscale Networks

2017 ◽  
Vol 32 (2) ◽  
pp. 23-30 ◽  
Author(s):  
Jesse Glass ◽  
Zoran Obradovic
Keyword(s):  
Structured Regression ◽  
Multiscale Networks

Structured Gaussian Process Regression of Music Mood

2020 ◽  
Vol 176 (2) ◽  
pp. 183-203
Author(s):  
Santosh Chapaneri ◽  
Deepak Jayaswal
Keyword(s):  
Gaussian Process ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Gaussian Mixture ◽  
Feature Representation ◽  
Affective Content ◽  
Variational Bayesian Inference ◽  
Benchmark Datasets ◽  
Valence And Arousal ◽  
Structured Regression

Modeling the music mood has wide applications in music categorization, retrieval, and recommendation systems; however, it is challenging to computationally model the affective content of music due to its subjective nature. In this work, a structured regression framework is proposed to model the valence and arousal mood dimensions of music using a single regression model at a linear computational cost. To tackle the subjectivity phenomena, a confidence-interval based estimated consensus is computed by modeling the behavior of various annotators (e.g. biased, adversarial) and is shown to perform better than using the average annotation values. For a compact feature representation of music clips, variational Bayesian inference is used to learn the Gaussian mixture model representation of acoustic features and chord-related features are used to improve the valence estimation by probing the chord progressions between chroma frames. The dimensionality of features is further reduced using an adaptive version of kernel PCA. Using an efficient implementation of twin Gaussian process for structured regression, the proposed work achieves a significant improvement in R2 for arousal and valence dimensions relative to state-of-the-art techniques on two benchmark datasets for music mood estimation.

scholarly journals Secretary letter-shapes in County Durham

2017 ◽  
Vol 51 (s38) ◽  
Author(s):  
Jacob Thaisen
Keyword(s):  
Regression Models ◽  
Quantitative Methods ◽  
English Language ◽  
Text Type ◽  
Individual Letter ◽  
Word Position ◽  
History Of ◽  
Structured Regression ◽  
The Individual ◽  
Letter Shape

AbstractThis paper applies quantitative methods in palaeography. It develops tree-structured regression models of the palaeographical variation found in a synchronic corpus of texts written in orthographically less standardised late Middle English and establishes their accuracy. There are sixteen models, each one relating to a letter-shape known to distinguish the Gothic cursive scripts Anglicana and Secretary. The models predict the presence of the individual letter-shape from one or more of the following variables, in no particular order: (1) localisation of texts’ orthographic variation; (2) text-type; and (3) in-word position. The discussion asks why several Secretary letter-shapes cluster in documents localisable to County Durham and the area further north, given the script’s association with (a) institutions of national administration in the London-Westminster area and (b) orthographic standardisation. It concludes that the linguistics and the palaeography do not co-vary during this period in the history of the English language and suggests that it may illuminate studies of the gradient between Anglicana and Secretary to pay attention to provincial centres, not least Durham.

Warm Season Lightning Probability Prediction for Canada and the Northern United States

2005 ◽  
Vol 20 (6) ◽  
pp. 971-988 ◽  
Author(s):  
William R. Burrows ◽  
Colin Price ◽  
Laurence J. Wilson
Keyword(s):  
Statistical Models ◽  
Prediction Models ◽  
Grid Point ◽  
Warm Season ◽  
Precipitable Water ◽  
Large Area ◽  
Maximum Probability ◽  
The North ◽  
The Mean ◽  
Structured Regression

Abstract Statistical models valid May–September were developed to predict the probability of lightning in 3-h intervals using observations from the North American Lightning Detection Network and predictors derived from Global Environmental Multiscale (GEM) model output at the Canadian Meteorological Centre. Models were built with pooled data from the years 2000–01 using tree-structured regression. Error reduction by most models was about 0.4–0.7 of initial predictand variance. Many predictors were required to model lightning occurrence for this large area. Highest ranked overall were the Showalter index, mean sea level pressure, and troposphere precipitable water. Three-hour changes of 500-hPa geopotential height, 500–1000-hPa thickness, and MSL pressure were highly ranked in most areas. The 3-h average of most predictors was more important than the mean or maximum (minimum where appropriate). Several predictors outranked CAPE, indicating it must appear with other predictors for successful statistical lightning prediction models. Results presented herein demonstrate that tree-structured regression is a viable method for building statistical models to forecast lightning probability. Real-time forecasts in 3-h intervals to 45–48 h were made in 2003 and 2004. The 2003 verification suggests a hybrid forecast based on a mixture of maximum and mean forecast probabilities in a radius around a grid point and on monthly climatology will improve accuracy. The 2004 verification shows that the hybrid forecasts had positive skill with respect to a reference forecast and performed better than forecasts defined by either the mean or maximum probability at most times. This was achieved even though an increase of resolution and change of convective parameterization scheme were made to the GEM model in May 2004.

Evaluating Recurring Traffic Congestion using Change Point Regression and Random Variation Markov Structured Model

2018 ◽  
Vol 2672 (20) ◽  
pp. 63-74 ◽  
Author(s):  
Emmanuel Kidando ◽  
Ren Moses ◽  
Thobias Sando ◽  
Eren E. Ozguven
Keyword(s):  
Flow Rate ◽  
Change Point ◽  
Traffic Congestion ◽  
Transition Probability ◽  
Information Criterion ◽  
Credible Interval ◽  
Probit Regression ◽  
Peak Period ◽  
Structured Regression ◽  
Random Variation

This study develops a probabilistic framework that evaluates the dynamic evolution of recurring traffic congestion (RTC) using the random variation Markov structured regression (MSR). This approach integrates the Markov chains assumption and probit regression. The analysis was performed using traffic data from a section of Interstate 295 located in Jacksonville, Florida. These data were aggregated on a 5-minute basis for 1 year (2015). Estimating discrete traffic states to apply the MSR model, this study established a definition of traffic congestion using Bayesian change point regression (BCR), in which the speed–occupancy relationship was explored. The MSR model with flow rate as a covariate was then used to estimate the probability of RTC occurrence. Findings from the BCR model suggest that the morning peak congested state occurs once speed is below 58 miles per hour (mph), whereas the evening peak period occurs at a speed below 55 mph. Evaluating the dynamics of traffic states over time, the Bayesian information criterion confirmed the hypothesis that a first-order Markov chain assumption is sufficient to characterize RTC. Moreover, the flow rate in the MSR model was found to be statistically significant in influencing the transition probability between the traffic regimes at 95% posterior credible interval. The knowledge of RTC transition explained by the approaches presented here will facilitate developing effective intervention strategies for mitigating RTC.

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