Sparse Boosting Based Machine Learning Methods For High Dimensional Data
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Sparse Boosting Based Machine Learning Methods For High Dimensional Data
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Author : Mu Yue
language : en
Publisher:
Release Date : 2020
Sparse Boosting Based Machine Learning Methods For High Dimensional Data written by Mu Yue and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic books categories.
In high-dimensional data, penalized regression is often used for variable selection and parameter estimation. However, these methods typically require time-consuming cross-validation methods to select tuning parameters and retain more false positives under high dimensionality. This chapter discusses sparse boosting based machine learning methods in the following high-dimensional problems. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers. Then, a two-step sparse boosting method to carry out the variable selection and the model-based prediction is studied for the high-dimensional longitudinal observations measured repeatedly over time. Finally, a multi-step sparse boosting method to identify patient subgroups that exhibit different treatment effects is studied for the high-dimensional dense longitudinal observations. This chapter intends to solve the problem of how to improve the accuracy and calculation speed of variable selection and parameter estimation in high-dimensional data. It aims to expand the application scope of sparse boosting and develop new methods of high-dimensional survival analysis, longitudinal data analysis, and subgroup analysis, which has great application prospects.
Computational Statistics And Applications
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Author : Ricardo López-Ruiz
language : en
Publisher: BoD – Books on Demand
Release Date : 2022-04-06
Computational Statistics And Applications written by Ricardo López-Ruiz and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-06 with Computers categories.
Nature evolves mainly in a statistical way. Different strategies, formulas, and conformations are continuously confronted in the natural processes. Some of them are selected and then the evolution continues with a new loop of confrontation for the next generation of phenomena and living beings. Failings are corrected without a previous program or design. The new options generated by different statistical and random scenarios lead to solutions for surviving the present conditions. This is the general panorama for all scrutiny levels of the life cycles. Over three sections, this book examines different statistical questions and techniques in the context of machine learning and clustering methods, the frailty models used in survival analysis, and other studies of statistics applied to diverse problems.
Statistics For High Dimensional Data
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Author : Peter Bühlmann
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-08
Statistics For High Dimensional Data written by Peter Bühlmann and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-08 with Mathematics categories.
Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
Metric Learning
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Author : Aurélien Muise
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Metric Learning written by Aurélien Muise and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' Biographies
Machine Learning Methods For High Dimensional Data And Multimodal Single Cell Data
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Author : Zixuan Song
language : en
Publisher:
Release Date : 2022
Machine Learning Methods For High Dimensional Data And Multimodal Single Cell Data written by Zixuan Song and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Hands On Gradient Boosting With Xgboost And Scikit Learn
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Author : Corey Wade
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-10-16
Hands On Gradient Boosting With Xgboost And Scikit Learn written by Corey Wade and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Computers categories.
Get to grips with building robust XGBoost models using Python and scikit-learn for deployment Key Features Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and tricks and gain innovative insights from XGBoost Kaggle winners Book Description XGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently. The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You'll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You'll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. Details in XGBoost are explored with a focus on speed enhancements and deriving parameters mathematically. With the help of detailed case studies, you'll practice building and fine-tuning XGBoost classifiers and regressors using scikit-learn and the original Python API. You'll leverage XGBoost hyperparameters to improve scores, correct missing values, scale imbalanced datasets, and fine-tune alternative base learners. Finally, you'll apply advanced XGBoost techniques like building non-correlated ensembles, stacking models, and preparing models for industry deployment using sparse matrices, customized transformers, and pipelines. By the end of the book, you'll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed. What you will learn Build gradient boosting models from scratch Develop XGBoost regressors and classifiers with accuracy and speed Analyze variance and bias in terms of fine-tuning XGBoost hyperparameters Automatically correct missing values and scale imbalanced data Apply alternative base learners like dart, linear models, and XGBoost random forests Customize transformers and pipelines to deploy XGBoost models Build non-correlated ensembles and stack XGBoost models to increase accuracy Who this book is for This book is for data science professionals and enthusiasts, data analysts, and developers who want to build fast and accurate machine learning models that scale with big data. Proficiency in Python, along with a basic understanding of linear algebra, will help you to get the most out of this book.
Machine Learning Techniques For High Dimensional Data
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Author : Yuan Chi
language : en
Publisher:
Release Date : 2015
Machine Learning Techniques For High Dimensional Data written by Yuan Chi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Statistical Foundations Of Data Science
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Author : Jianqing Fan
language : en
Publisher: CRC Press
Release Date : 2020-09-21
Statistical Foundations Of Data Science written by Jianqing Fan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-21 with Mathematics categories.
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Data Sparse Algorithms And Mathematical Theory For Large Scale Machine Learning Problems
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Author : Ruoxi Wang
language : en
Publisher:
Release Date : 2018
Data Sparse Algorithms And Mathematical Theory For Large Scale Machine Learning Problems written by Ruoxi Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
This dissertation presents scalable algorithms for high-dimensional large-scale datasets in machine learning applications. The ability to generate data at the scale of millions and even billions has increased rapidly, posing computational challenges to most machine learning algorithms. I propose fast kernel-matrix-based algorithms that avoid intensive kernel matrix operations and neural-network-based algorithms that efficiently learn feature interactions. My contributions include: 1) A structured low-rank approximation method--the Block Basis Factorization (BBF)--that reduces the training time and memory for kernel methods from quadratic to linear and enjoys better accuracy than state-of-art kernel approximation algorithms. 2) Mathematical theories for the ranks of RBF kernel matrices generated from high-dimensional datasets. 3) A parallel black-box fast multipole method (FMM) software library--PBBFMM3D--that evaluates particle interactions in 3D. 4) A neural network--the Deep & Cross Network (DCN)--for web-scale data predictions that requires no exhaustive feature searching nor manual feature engineering and efficiently learns bounded-degree feature interactions combined with complex deep representations. Chapter 2 presents BBF, which accelerates kernel methods by factorizing an n by n kernel matrix into a sparse representation with O(n) nonzero entries as compared to O(n^2). By identifying the low-rank properties of certain blocks, BBF extends the domain of applicability of low-rank approximation methods to the cases where traditional low-rank approximations are inefficient. By leveraging the knowledge from numerical linear algebra and randomized algorithms, the factorization can be constructed in O(n) time complexity while being accurate and stable. Our empirical results demonstrate the stability and superiority over the state-of-art kernel approximation algorithms. Chapter 3 presents a theoretical analysis of the RBF kernel matrix rank. Our three main results are as follows. First, we study the kernel rank, which for a fixed precision grows algebraically with the data dimension (in the worst case), and where the power is related to the accuracy. Second, we derive precise error bounds for the low-rank approximation in the L_infty norm in terms of the function smoothness and the domain diameters. And third, we analyze a group pattern in the magnitude of the singular values of the RBF kernel matrix. We explain this pattern by a grouping of the expansion terms in the kernel's low-rank representation. Empirical results verify the theoretical results. Chapter 4 presents PBBFMM3D, which is a parallel implementation of the fast multipole method (FMM) for evaluating pair-wise particle interactions (matrix-vector product) in three dimensions. PBBFMM3D applies to all non-oscillatory smooth kernel functions and only requires the kernel evaluations at data points. It has O(N) complexity as opposed to O(N^2) complexity from a direct computation. We discuss several algorithmic improvements and performance optimizations, such as shared memory parallelism using OpenMP. We present convergence and scalability results, as well as applications including particle potential evaluations, which frequently occur in PDE-related simulations, and covariance matrix computations that are essential parts in parameter estimation techniques, e.g., Kriging and Kalman filtering. Chapter 5 presents DCN, which is designed for datasets with dense and sparse combined features and enables automatic and efficient feature learning. Feature engineering is the key to the success of prediction models; however, the process often requires manual feature engineering or exhaustive searching. DCN combines a deep neural network that learns complex but implicit feature interactions, with a novel cross network that is more efficient in learning certain explicit bounded-degree feature interactions. Our experimental results have demonstrated its superiority over the state-of-art algorithms on the click-through-rate prediction dataset and dense classification dataset, in terms of both model accuracy and memory usage.
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