A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Boosted Fuzzy Granular Regression Trees
2021
Mathematical Problems in Engineering
Preserved Fulltext
Based on granular computing and ensemble learning, we convert the regression problem into granular space equivalently to solve and proposed boosted fuzzy granular regression trees (BFGRT) to predict a ...
Then, we design new operators and metrics of fuzzy granules to build fuzzy granular rule base. Finally, a fuzzy granular regression tree (FGRT) in the fuzzy granular space is presented. ...
granular regression trees as the final regression value. ...
doi:10.1155/2021/9958427
fatcat:4ni3ysxau5e3vp5fjbxwriumaq
Granular Elastic Network Regression with Stochastic Gradient Descent
2022
Mathematics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Further, the granular norm and the granular vector norm are defined to design the granular loss function and construct the granular elastic network regression model. ...
We found that the granular elasticity network has the advantage of good fit compared with the traditional linear regression model. ...
We will propose a new granulation approach to apply granular regression to big data. In future work, we will apply the granular regression system to the nonlinear regression system. ...
doi:10.3390/math10152628
fatcat:36luseab5fa3jiey5divsmopqi
Genetic interval neural networks for granular data regression
2014
Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Granular data and granular models offer an interesting tool for representing data in problems involving uncertainty, inaccuracy, variability and subjectivity have to be taken into account. ...
The proposed MLP comes with interval-valued weights and biases, and is trained using a genetic algorithm designed to fit data with different levels of granularity. ...
Our focus is on an interval regression problem. The first conceptualization of neural networks for processing granular data was introduced by Pedrycz and Vukovich [28] . ...
doi:10.1016/j.ins.2012.12.049
fatcat:nje3e742m5fnffv5slqemowfx4
Combining Piecewise Linear Regression and a Granular Computing Framework for Financial Time Series Classification
2014
Proceedings of the International Conference on Evolutionary Computation Theory and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In this paper we propose a new method for a trend prediction on financial time series combining a Linear Piecewise Regression with a granular computing framework. ...
In this paper we introduce a new Hybrid Classification Algorithm based on a Piecewise Linear Regression (PLR) preprocessing of raw data within a Granular Computing (GrC) framework (Bargiela and Pedrycz ...
Linear Piecewise Regression First we collect raw data about the prices of a stock, in a fixed time interval, from a specific data file. ...
doi:10.5220/0005127402810288
dblp:conf/ijcci/ModugnoPR14
fatcat:ynwfklj5gjgq5bti4o77v4572q
Spontaneous Regression of Methotrexate-related Lymphoproliferative Disorder with T-cell Large Granular Lymphocytosis
2015
Internal medicine (Tokyo. 1992)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
We herein show that spontaneous regression of MTX-LPDs is associated with the development of significant and transient T-cell large granular lymphocyte (T-LGL) lymphocytosis induced by MTX withdrawal. ...
Since T-LGLs show strong cytotoxicity, their expansion may contribute to the spontaneous regression of lymphoma. ...
regression (10) . ...
doi:10.2169/internalmedicine.54.4207
pmid:26328653
fatcat:lpab2ntwvvaopgld2l7f43rmcq
Adaptive and iterative least squares support vector regression based on quadratic Renyi entropy
2008
2008 IEEE International Conference on Granular Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The regression parameters are calculated by incremental learning and the calculation of inversing a large scale matrix is avoided. So the running speed is improved. ...
i e b x w y i i T i ,..., 1 , ) ( = + + = ϕ (3) This corresponds to a form of ridge regression. ...
Calculate α and b according to Eq.(8) and obtain the
regression function
W
x
f
|
)
(
. ...
doi:10.1109/grc.2008.4664732
dblp:conf/grc/JiangSZWL08
fatcat:cmmhiw7fbjgwtpi2cdtgvtgywm
Support Vector Regression Modelling of an Aerobic Granular Sludge in Sequential Batch Reactor
2021
Membranes
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures. ...
The results showed the potential of SVR for simulating the complex aerobic granulation process and providing an excellent tool to help predict the behaviour in aerobic granular reactors of wastewater treatment ...
The biological treatment of this study is based on aerobic granular sludge (AGS) instead of conventional activated sludge systems. ...
doi:10.3390/membranes11080554
pmid:34436317
pmcid:PMC8400290
fatcat:uc5zwncn4rccpphrlh4gpxg3pi
The impact of test suite granularity on the cost-effectiveness of regression testing
2002
Proceedings of the 24th international conference on Software engineering - ICSE '02
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2008; you can also visit the original URL.
The file type is application/pdf.
To address this lack, we conducted several controlled experiments, examining the e ects of test suite granularity on the costs and bene ts of several regression testing methodologies across six releases ...
Regression testing is an expensive testing process used to validate software following modi cations. The cost-e ectiveness of regression testing techniques varies with characteristics of test suites. ...
Granularity and Regression Test Selection We begin by exploring the impact of test suite granularity on regression test selection techniques. ...
doi:10.1145/581339.581358
dblp:conf/icse/RothermelEMKD02
fatcat:uiwle23u3vbg3dxbsccippfnou
The impact of test suite granularity on the cost-effectiveness of regression testing
2002
Proceedings of the 24th international conference on Software engineering - ICSE '02
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2008; you can also visit the original URL.
The file type is application/pdf.
To address this lack, we conducted several controlled experiments, examining the e ects of test suite granularity on the costs and bene ts of several regression testing methodologies across six releases ...
Regression testing is an expensive testing process used to validate software following modi cations. The cost-e ectiveness of regression testing techniques varies with characteristics of test suites. ...
Granularity and Regression Test Selection We begin by exploring the impact of test suite granularity on regression test selection techniques. ...
doi:10.1145/581356.581358
fatcat:sslwcpmj2nbkzd7kxqab5iejiq
RFIMiner: A regression-based algorithm for recently frequent patterns in multiple time granularity data streams
2007
Applied Mathematics and Computation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
First, a regression-based data stream model is proposed to differentiate new and old transactions. ...
FP-stream employs a novel titled-time windows technique and mines frequent itemsets at multiple time granularities. ...
Therefore, new transactions are always in a smaller and finer time granularity than old ones. Again, transferences between two time granularities cause the regression of transactions. ...
doi:10.1016/j.amc.2006.06.115
fatcat:45kiaxjat5dsxgm6wne6gzpa4u
Adaptive and iterative least squares support vector regression based on quadratic Renyi entropy
2010
International Journal of Granular Computing Rough Sets and Intelligent Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The regression parameters are calculated by incremental learning and the calculation of inversing a large scale matrix is avoided. So the running speed is improved. ...
i e b x w y i i T i ,..., 1 , ) ( = + + = ϕ (3) This corresponds to a form of ridge regression. ...
Calculate α and b according to Eq.(8) and obtain the
regression function
W
x
f
|
)
(
. ...
doi:10.1504/ijgcrsis.2010.029579
fatcat:zjv2cjgqzzgrhnsth7w6n5whc4
Practical Validity of the Continuity Assumption in Regression Discontinuity Designs: Granularity, Degree of Focus, and Multiple Thresholds
2020
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
"Regression discontinuity analysis: an alternative to the ex-post facto experiment." Journal of Educational Psychology, 51:309-317. ...
I discuss how the number of problematic, untestable variables can vary according to granularity of the data and degree of focus at the discontinuity threshold and suggest possible manipulations, including ...
For an overview of the principles of regression discontinuity design, see Hahn et al. (2001) and for a more complete guide to common use, see Lee and Lemieux (2010) . ...
doi:10.13140/rg.2.2.22163.02082
fatcat:3yacmway6jd2lp56ns2td67b6a
The impact of test suite granularity on the cost-effectiveness of regression testing
Proceedings of the 24th International Conference on Software Engineering. ICSE 2002
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2008; you can also visit the original URL.
The file type is application/pdf.
To address this lack, we conducted several controlled experiments, examining the e ects of test suite granularity on the costs and bene ts of several regression testing methodologies across six releases ...
Regression testing is an expensive testing process used to validate software following modi cations. The cost-e ectiveness of regression testing techniques varies with characteristics of test suites. ...
Granularity and Regression Test Selection We begin by exploring the impact of test suite granularity on regression test selection techniques. ...
doi:10.1109/icse.2002.1007962
fatcat:sixdbbwz3ja3hoci7lbd64t3qa
Successive Overrelaxation for Support Vector Regression
[chapter]
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2005; you can also visit the original URL.
The file type is application/pdf.
Training a SVR (support vector regression) requires the solution of a very large QP (quadratic programming) optimization problem. ...
In this work, we propose a new way to make SVR (support vector regression) have the similar mathematic form as that of a support vector classification, and derive a generalization of SOR to handle regression ...
Because of these constraints, Shevade [6] and Chih-Jen [7] generalized SMO so that it can handle regression problems. ...
doi:10.1007/3-540-39205-x_109
dblp:conf/rsfdgrc/QuanYY03
fatcat:35k6a4rf3ndxdej3qrj7xjhdoe
Page 932 of SMPTE Motion Imaging Journal Vol. 92, Issue 9
[page]
1983
SMPTE Motion Imaging Journal
Preserved Fulltext
The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
Also, the re- gression analysis of the perceived graininess on the combined blue-, green-, and red-filter RMS-granular-
T of
Psy. Sc. Gr. Model coefficient
F of
regression R? of regression
pt? ...
According to Figs. 5, 6a, and 7 and Chart 8, the visually perceived grain-
Chart 8 — Regression Analysis
iness is apparently largely determined by the granularity of the magenta and the cyan layer and ...
« Previous
Showing results 1 — 15 out of 91,177 results