2,187 resources related to Regression analysis
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The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore
2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC2019) will be held in the south of Europe in Bari, one of the most beautiful and historical cities in Italy. The Bari region’s nickname is “Little California” for its nice weather and Bari's cuisine is one of Italian most traditional , based of local seafood and olive oil. SMC2019 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report up-to-the-minute innovations and developments, summarize stateof-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems and cybernetics. Advances have importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience, and thereby improve quality of life.
International Geosicence and Remote Sensing Symposium (IGARSS) is the annual conference sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS), which is also the flagship event of the society. The topics of IGARSS cover a wide variety of the research on the theory, techniques, and applications of remote sensing in geoscience, which includes: the fundamentals of the interactions electromagnetic waves with environment and target to be observed; the techniques and implementation of remote sensing for imaging and sounding; the analysis, processing and information technology of remote sensing data; the applications of remote sensing in different aspects of earth science; the missions and projects of earth observation satellites and airborne and ground based campaigns. The theme of IGARSS 2019 is “Enviroment and Disasters”, and some emphases will be given on related special topics.
The Annual IEEE PES General Meeting will bring together over 2900 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.
IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
Tsinghua Science and Technology, 2008
The widespread use of Internet accelerates the rapid development of business to customer electronic commerce. To reduce information overload and help their customers to make better purchase decisions, e-commerce websites are beginning to use online recommendations. This paper compares the effectiveness of three types of online recommendations, the personalized recommendation, best sellers, and consumers' reviews, which are widely used in ...
Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), 2007
In recent decades, soft computing techniques have broadly applied to solve complex problems. Among the soft computing techniques, artificial immune system (AIS) have appeared as a new approach dealing with classification problems. In this paper, an AIS algorithm is developed and applied to a two- group classification problem. An example of Taiwanese banking industry is discussed and the financial ratios ...
2013 Sixth International Conference on Business Intelligence and Financial Engineering, 2013
Researches on giving, charity switching behaviour and donors' loyalty can easily be found in a half dozen American states or regions. However, there is still a gap between non-western countries on similar researches in terms of scope and depth. With such deficiency, this study attempts to make use of the case in Hong Kong in order to fill the research ...
Technology-Based Re-Engineering Engineering Education Proceedings of Frontiers in Education FIE'96 26th Annual Conference, 1996
Based on multivariate data collected over three years, linear regression equations are developed and used to assess student learning in large sections of engineering economy taught at Virginia Tech. In each year (1993, 1994 and 1995), more than 350 students in the fall semester voluntarily participated in this research. This paper presents the principal findings of the study and demonstrates ...
Conference Record Southcon, 1994
The I-V characteristics of metal oxide semiconductors has long been a subject of research by many scientists in the field of microelectronics. It is generally accepted that the current conduction in the thin gate oxide is due to the Fowler-Nordheim tunneling emission. However, there is quite a variation in the electrical properties and the Fowler-Nordheim tunneling parameters reported by researchers. ...
Single Frame Super Resolution: Fuzzy Rule-Based and Gaussian Mixture Regression Approaches
Linear Regression: Intro to Machine Learning Workshop - IEEE Region 4 Presentation
IMS 2011 Microapps - Yield Analysis During EM Simulation
IMS 2012 Microapps - Improve Microwave Circuit Design Flow Through Passive Model Yield and Sensitivity Analysis
New Approach of Vehicle Electrification: Analysis of Performance and Implementation Issue
A Flexible Testbed for 5G Waveform Generation and Analysis: MicroApps 2015 - Keysight Technologies
IMS 2011 Microapps - A Practical Approach to Verifying RFICs with Fast Mismatch Analysis
IMS MicroApps: Multi-Rate Harmonic Balance Analysis
Similarity and Fuzzy Logic in Cluster Analysis
Spectrum Analysis: RF Boot Camp
Surgical Robotics: Analysis and Control Architecture for Semiautonomous Robotic Surgery
IMS 2012 Microapps - Generation and Analysis Techniques for Cost-efficient SATCOM Measurements Richard Overdorf, Agilent
IMS 2011 Microapps - Tools for Creating FET and MMIC Thermal Profiles
Zohara Cohen AMA EMBS Individualized Health
IMS 2011 Microapps - STAN Tool: A New Method for Linear and Nonlinear Stability Analysis of Microwave Circuits
IMS 2011 Microapps - Remcom's XFdtd and Wireless InSite: Advanced Tools for Advanced Communication Systems Analysis
Learning through Deterministic Assignment of Hidden Parameter
Sparse Fuzzy Modeling - Nikhil R Pal - WCCI 2016
Network Analysis: RF Boot Camp
The widespread use of Internet accelerates the rapid development of business to customer electronic commerce. To reduce information overload and help their customers to make better purchase decisions, e-commerce websites are beginning to use online recommendations. This paper compares the effectiveness of three types of online recommendations, the personalized recommendation, best sellers, and consumers' reviews, which are widely used in e-commerce. This research used a laboratory experiment combined with a questionnaire. This paper also establishes an integrated model of the facts that influence recommendation effectiveness.
In recent decades, soft computing techniques have broadly applied to solve complex problems. Among the soft computing techniques, artificial immune system (AIS) have appeared as a new approach dealing with classification problems. In this paper, an AIS algorithm is developed and applied to a two- group classification problem. An example of Taiwanese banking industry is discussed and the financial ratios of each bank from 1998 to 2002 were collected. This system has to distinguish the operational performance (good or bad) of each bank to offer a reference material for the managers or investors. The performance of AIS is compared with other five early warning systems, namely, genetic neural networks (GNN), case-based reasoning (CBR), backpropagation neural network (BPN), logistic regression analysis (LR), and quadratic discriminant analysis (QDA). The result indicates that the proposed AIS is over 10% better than the three soft computing early warning systems (GNN, CBR and BPN). The AIS outperforms the statistical early warning systems (LR and QDA) at least 24%.
Researches on giving, charity switching behaviour and donors' loyalty can easily be found in a half dozen American states or regions. However, there is still a gap between non-western countries on similar researches in terms of scope and depth. With such deficiency, this study attempts to make use of the case in Hong Kong in order to fill the research gap by developing localized tools to understand the factors impacting the donation switching behaviour which can help the Non-Governmental Organizations (NGOs) to develop fundraising strategies that fit the local needs. This study shows that NGOs should keep their organization unique with ongoing updates to/from donors. Also, they should not solely rely on traditional classification factors like donation amount to identify loyalty donors.
Based on multivariate data collected over three years, linear regression equations are developed and used to assess student learning in large sections of engineering economy taught at Virginia Tech. In each year (1993, 1994 and 1995), more than 350 students in the fall semester voluntarily participated in this research. This paper presents the principal findings of the study and demonstrates the use of multivariate linear regression for evaluating student performance (learning) in engineering economy.
The I-V characteristics of metal oxide semiconductors has long been a subject of research by many scientists in the field of microelectronics. It is generally accepted that the current conduction in the thin gate oxide is due to the Fowler-Nordheim tunneling emission. However, there is quite a variation in the electrical properties and the Fowler-Nordheim tunneling parameters reported by researchers. This is partially due to the presence of oxide charges which affects the I-V characteristics of the gate oxide and thereby the extracted parameters. The current conduction in the thin gate oxide is complicated; different mechanisms may dominate at different voltage regions. In this study, multiple regression analysis is utilized to determine the basic conduction process and to clarify the discrepancies in the literature.
The paper is concerned with the application of multivariate regression analysis to the reduction of a many-variable control problem and to the identification of linear and nonlinear time-varying processes. Reduction is performed by grouping the input and output variables of a many-variable process into a small number of groups of variables. Control is exercised in terms of a few variables, each representing such a group. Regression is further applied to the identification of linear and nonlinear multivariable processes where no apriori information of the dynamic characteristics is available. The resulting identification subroutines are conveniently incorporated in control procedures based on predictive-adaptive control and on dynamic programming.
To fill the data missed and to make statistical activities more effectively response, according to the missing mechanism the data is missing at random (MAR) and missing completely at random (MCAR) conditions, several methods for missing data, multiple imputation, kernel function of non-parametric regression analysis are introduced. The kernel function of non-parametric regression analysis is analyzed with emphasis, and the corresponding functions and the theorem for their analytical methods are discussed. Then, an example is introduced to verify the accuracy of this method. By analyzing, the results show that the data filled according to the methods of kernel function of non- parametric regression analysis can replace the original data, and the statistical results are identified with the original ones.
An incorrect inequality was applied in the proof of Theorem 2, in the above- mentioned paper. In this note, we correct the proof. The revised theorem requires bounded regressors.
A new technique of fusing genetic algorithms with Fuzzy ARTMAP is proposed. This method selects the appropriate autoregressive model order for EEG signals and consequently classifies these signals into their respective different mental tasks. The experimental results show that this method outperforms other statistical autoregressive model order selection methods like Akaike Information Criterion, Final Prediction Error and reflection coefficient.
Against the low efficiency of training on large-scale SVM, a reduction approach based on kernel distance clustering is proposed. The kernel distance's formulation is brought in to cluster the highly-dimensioned dataset, and the clustering step will reduce a large amount of unsupport vectors during training, thereby, the training time will decrease. The experiments show that this new training algorithm is able to speed up the training process and improve the classification's precision.
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