Comparison Of Multiple Model Algorithms
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Comparison Of Multiple Model Algorithms
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Author : Surya P. Jakhotia
language : en
Publisher:
Release Date : 2000
Comparison Of Multiple Model Algorithms written by Surya P. Jakhotia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.
Eifel Museen
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Author :
language : en
Publisher:
Release Date : 1997
Eifel Museen written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.
Preventing Multiple Comparisons Problems In Data Exploration And Machine Learning
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Author : Nikolaos Koulouris
language : en
Publisher:
Release Date : 2020
Preventing Multiple Comparisons Problems In Data Exploration And Machine Learning written by Nikolaos Koulouris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
More data means more opportunity for a researcher to test more hypotheses until he discovers an interesting finding. This increases the probability of arriving at a false conclusion purely by chance and is known as the multiple comparisons problem. Data exploration systems facilitate exploring big data by automatically testing thousands of hypotheses in order to find the most interesting ones. In machine learning analysts repeatedly test a model's performance on a holdout dataset until they find the one with the best performance. Auto-ML systems try to automate this model selection process. In both cases, testing for more things means a higher probability of making a statement purely by chance. This dissertation examines how the multiple comparisons problem appears in the field of data exploration and machine learning. In both cases we propose techniques that exploit some structure that appears in the field to improve upon existing techniques and reduce the consequences of multiple comparisons. We present VigilaDE, the first data exploration system that utilizes the hierarchical structure of the data in order to control false discoveries. A plethora of real-world datasets already have domain-specific hierarchies that describe the relationship between variables. VigilaDE utilizes these hierarchies to guide the exploration towards interesting discoveries while controlling false discoveries and, as a result, increasing statistical power. Through extensive experiments with real-world data, simulations and theoretical analysis we show that our data exploration algorithms can find up to 2.7x more true discoveries in the data against the baseline while controlling the number of false discoveries. In machine learning, the consequence of testing multiple different models is overfitting. We present an experimental analysis of ThresholdOut, the state of the art algorithm for avoiding overfitting a holdout dataset. The main limitation of ThresholdOut is setting its parameters. We present Auto-Set, an automated way to set its parameters for feature selection. Specifically in feature selection the order of the models that we test on a holdout dataset has a very specific structure. We utilize this structure in Auto Adjust Threshold, a novel feature selection algorithm that avoids overfitting a holdout dataset and show that it outperforms existing algorithms.
Ensemble Learning Algorithms With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2021-04-26
Ensemble Learning Algorithms With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-26 with Computers categories.
Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.
Radar Data Processing With Applications
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Author : He You
language : en
Publisher: John Wiley & Sons
Release Date : 2016-08-01
Radar Data Processing With Applications written by He You and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-01 with Technology & Engineering categories.
Radar Data Processing with Applications Radar Data Processing with Applications He You, Xiu Jianjuan, Guan Xin, Naval Aeronautical and Astronautical University, China A summary of thirty years’ worth of research, this book is a systematic introduction to the theory, development, and latest research results of radar data processing technology. Highlights of the book include sections on data pre-processing technology, track initiation, and data association. Readers are also introduced to maneuvering target tracking, multiple target tracking termination, and track management theory. In order to improve data analysis, the authors have also included group tracking registration algorithms and a performance evaluation of radar data processing. Presents both classical theory and development methods of radar data processing Provides state-of-the-art research results, including data processing for modern radars and tracking performance evaluation theory Includes coverage of performance evaluation, registration algorithm for radar networks, data processing of passive radar, pulse Doppler radar, and phased array radar Features applications for those engaged in information engineering, radar engineering, electronic countermeasures, infrared techniques, sonar techniques, and military command Radar Data Processing with Applications is a handy guide for engineers and industry professionals specializing in the development of radar equipment and data processing. It is also intended as a reference text for electrical engineering graduate students and researchers specializing in signal processing and radars.
A Comparison Of Machine Learning Algorithms In Predicting Nonnormal Continuous Outcome Variables
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Author : Erin Crangle
language : en
Publisher:
Release Date : 2022
A Comparison Of Machine Learning Algorithms In Predicting Nonnormal Continuous Outcome Variables written by Erin Crangle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Machine learning categories.
Machine learning is a type of data analysis that creates prediction models by learning from a portion of the data set. These algorithms can be used in many disciplines to answer complex questions and hypotheses. There are many available algorithms, each with their own strengths and weaknesses. Much research has been compiled on each algorithm individually to show where they excel and provide context into many use cases. The purpose of this research project is to document a comparison of BART, Random Forest, and GBM; A few top machine learning algorithms on their ability to predict nonnormal continuous outcome variables. The results of this study could help determine which prediction models preform the most efficiently and accurately when building predictive models for nonnormal continuous outcome variables.
Kalman Filtering
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Author : Mohinder S. Grewal
language : en
Publisher: John Wiley & Sons
Release Date : 2015-02-02
Kalman Filtering written by Mohinder S. Grewal and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-02 with Technology & Engineering categories.
The definitive textbook and professional reference on Kalman Filtering – fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.
Practical Implementation Of Multiple Model Adaptive Estimation Using Neyman Pearson Based Hypothesis Testing And Spectral Estimation Tools
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Author : Peter D. Hanlon
language : en
Publisher:
Release Date : 1996-09-01
Practical Implementation Of Multiple Model Adaptive Estimation Using Neyman Pearson Based Hypothesis Testing And Spectral Estimation Tools written by Peter D. Hanlon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-09-01 with Flight control categories.
This study investigates and develops various modifications to the Multiple Model Adaptive Estimation (MMAE) algorithm. The standard MMAE uses a bank of Kalman filters, each based on a different model of the system. Each of the filters predict the system response, based on its system model, to a given input and form the residual difference between the prediction and sensor measurements of the system response. Model differences in the input matrix, output matrix, and state transition matrix, which respectively correspond to an actuator failure, sensor failure, and an incorrectly modeled flight condition for a flight control failure application, were investigated in this research. An alternative filter bank structure is developed that uses a linear transform on the residual from a single Kalman filter to produce the equivalent residuals of the other Kalman filters in the standard MMAE. A Neyman Pearson based hypothesis testing algorithm is developed that results in significant improvement in failure detection performance when compared to the standard hypothesis testing algorithm. Hypothesis testing using spectral estimation techniques is also developed which provides superior failure identification performance at extremely small input levels.
Artificial Intelligence Models Algorithms And Applications
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Author : Terje Solsvik Kristensen
language : en
Publisher: Bentham Science Publishers
Release Date : 2021-05-31
Artificial Intelligence Models Algorithms And Applications written by Terje Solsvik Kristensen and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-31 with Computers categories.
Artificial Intelligence: Models, Algorithms and Applications presents focused information about applications of artificial intelligence (AI) in different areas to solve complex problems. The book presents 8 chapters that demonstrate AI based systems for vessel tracking, mental health assessment, radiology, instrumentation, business intelligence, education and criminology. The book concludes with a chapter on mathematical models of neural networks. The book serves as an introductory book about AI applications at undergraduate and graduate levels and as a reference for industry professionals working with AI based systems.
Machine Learning And Data Science Blueprints For Finance
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Author : Hariom Tatsat
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-10-01
Machine Learning And Data Science Blueprints For Finance written by Hariom Tatsat and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-01 with Computers categories.
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations