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Author : Donna Karen Slonim
Publisher :
ISBN 13 :
Total Pages : 167 pages
Book Rating : 4.:/5 (359 download)
Download or read book Learning from Imperfect Data in Theory and Practice written by Donna Karen Slonim and published by . This book was released on 1996 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ronald K. Pearson
Publisher : SIAM
ISBN 13 : 1611976278
Total Pages : 581 pages
Book Rating : 4.6/5 (119 download)
Download or read book Mining Imperfect Data written by Ronald K. Pearson and published by SIAM. This book was released on 2020-09-10 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has been estimated that as much as 80% of the total effort in a typical data analysis project is taken up with data preparation, including reconciling and merging data from different sources, identifying and interpreting various data anomalies, and selecting and implementing appropriate treatment strategies for the anomalies that are found. This book focuses on the identification and treatment of data anomalies, including examples that highlight different types of anomalies, their potential consequences if left undetected and untreated, and options for dealing with them. As both data sources and free, open-source data analysis software environments proliferate, more people and organizations are motivated to extract useful insights and information from data of many different kinds (e.g., numerical, categorical, and text). The book emphasizes the range of open-source tools available for identifying and treating data anomalies, mostly in R but also with several examples in Python. Mining Imperfect Data: With Examples in R and Python, Second Edition presents a unified coverage of 10 different types of data anomalies (outliers, missing data, inliers, metadata errors, misalignment errors, thin levels in categorical variables, noninformative variables, duplicated records, coarsening of numerical data, and target leakage). It includes an in-depth treatment of time-series outliers and simple nonlinear digital filtering strategies for dealing with them, and it provides a detailed introduction to several useful mathematical characteristics of important data characterizations that do not appear to be widely known among practitioners, such as functional equations and key inequalities. While this book is primarily for data scientists, researchers in a variety of fields—namely statistics, machine learning, physics, engineering, medicine, social sciences, economics, and business—will also find it useful.
Author : Vasilis Kontonis
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (143 download)
Download or read book Learning from Imperfect Data written by Vasilis Kontonis and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The datasets used in machine learning and statistics are \emph{huge} and often \emph{imperfect},\textit{e.g.}, they contain corrupted data, examples with wrong labels, or hidden biases. Most existing approaches (i) produce unreliable results when the datasets are corrupted, (ii) are computationally inefficient, or (iii) come without any theoretical/provable performance guarantees. In this thesis, we \emph{design learning algorithms} that are \textbf{computationally efficient} and at the same time \textbf{provably reliable}, even when used on imperfect datasets. We first focus on supervised learning settings with noisy labels. We present efficient and optimal learners under the semi-random noise models of Massart and Tsybakov -- where the true label of each example is flipped with probability at most 50\% -- and an efficient approximate learner under adversarial label noise -- where a small but arbitrary fraction of labels is flipped -- under structured feature distributions. Apart from classification, we extend our results to noisy label-ranking. In truncated statistics, the learner does not observe a representative set of samples from the whole population, but only truncated samples, \textit{i.e.}, samples from a potentially small subset of the support of the population distribution. We give the first efficient algorithms for learning Gaussian distributions with unknown truncation sets and initiate the study of non-parametric truncated statistics. Closely related to truncation is \emph{data coarsening}, where instead of observing the class of an example, the learner receives a set of potential classes, one of which is guaranteed to be the correct class. We initiate the theoretical study of the problem, and present the first efficient learning algorithms for learning from coarse data.
Author : Sašo Džeroski
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (439 download)
Download or read book Learning relations from imperfect data written by Sašo Džeroski and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Yaoyao Liu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)
Download or read book Learning from Imperfect Data written by Yaoyao Liu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : 戈维峰
Publisher :
ISBN 13 :
Total Pages : 84 pages
Book Rating : 4.:/5 (114 download)
Download or read book Image Understanding from Imperfect Data Via Transfer Learning, Metric Learning, and Weakly Supervised Learning written by 戈维峰 and published by . This book was released on 2019 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jordi Solé-Casals
Publisher : MDPI
ISBN 13 : 3036512888
Total Pages : 316 pages
Book Rating : 4.0/5 (365 download)
Download or read book Machine Learning Methods with Noisy, Incomplete or Small Datasets written by Jordi Solé-Casals and published by MDPI. This book was released on 2021-08-17 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past years, businesses have had to tackle the issues caused by numerous forces from political, technological and societal environment. The changes in the global market and increasing uncertainty require us to focus on disruptive innovations and to investigate this phenomenon from different perspectives. The benefits of innovations are related to lower costs, improved efficiency, reduced risk, and better response to the customers’ needs due to new products, services or processes. On the other hand, new business models expose various risks, such as cyber risks, operational risks, regulatory risks, and others. Therefore, we believe that the entrepreneurial behavior and global mindset of decision-makers significantly contribute to the development of innovations, which benefit by closing the prevailing gap between developed and developing countries. Thus, this Special Issue contributes to closing the research gap in the literature by providing a platform for a scientific debate on innovation, internationalization and entrepreneurship, which would facilitate improving the resilience of businesses to future disruptions. Order Your Print Copy
Author : Stephen Boyd
Publisher : Now Publishers Inc
ISBN 13 : 160198460X
Total Pages : 138 pages
Book Rating : 4.6/5 (19 download)
Download or read book Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers written by Stephen Boyd and published by Now Publishers Inc. This book was released on 2011 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Author : Ronald K. Pearson
Publisher : SIAM
ISBN 13 : 0898715822
Total Pages : 309 pages
Book Rating : 4.8/5 (987 download)
Download or read book Mining Imperfect Data written by Ronald K. Pearson and published by SIAM. This book was released on 2005-04-01 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the problems that can occur in data mining, including their sources, consequences, detection and treatment.
Author : Thiago Christiano Silva
Publisher : Springer
ISBN 13 : 3319172905
Total Pages : 345 pages
Book Rating : 4.3/5 (191 download)
Download or read book Machine Learning in Complex Networks written by Thiago Christiano Silva and published by Springer. This book was released on 2016-01-28 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.
Author : Cheng Zhang (Ph. D. in computer science)
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Learning with Imperfect Data and Supervision for Visual Perception and Understanding written by Cheng Zhang (Ph. D. in computer science) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Having access to large amounts of well-balanced and well-labeled data is one of the most important components of learning-based perception systems. However, gathering and labeling high-quality training examples are often time-consuming, expensive, and error-prone. For example, human-curated datasets may come with a variety of unsatisfactory forms such as noisy, weak labels, and long-tailed data distributions. As a result, machine learning models trained with these imperfect data and annotations appear to be brittle in complex real-world scenarios. In this dissertation, we provide a set of techniques for taming imperfections of data and supervision in different applications, ranging from instance-level object perception to multimodal scene understanding. The core idea is to exploit the massive amounts of raw data and multimodal structures from different sources and integrate them with deep learning. The main body of this dissertation consists of three parts. First, we focus on long-tailed visual learning. Specifically, we investigate how to leverage heterogeneous, out-of-domain data to facilitate long-tailed object detection and instance segmentation. Second, going beyond instance-level perception, we explore data-efficient learning for visual question answering (VQA) with multiple levels of focus, including bootstrapping VQA datasets, and understanding multimodal information with graph-based representations, and debiasing VQA models with question-conditioned calibration. Third, we introduce a case study on building practical perception systems with imperfect, multi-sensory signals. We design and implement a real-time, accurate sports analysis system based on vision and motion sensor integration. In each part, we introduce the problem and present our solutions, followed by demonstrating the effectiveness of the developed methods on well-benchmarked datasets, tasks, and real-world applications.
Author : Garry Briscoe
Publisher : Intellect (UK)
ISBN 13 :
Total Pages : 386 pages
Book Rating : 4.3/5 (91 download)
Download or read book A Compendium of Machine Learning: Symbolic machine learning written by Garry Briscoe and published by Intellect (UK). This book was released on 1996 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is a relatively new branch of artificial intelligence. The field has undergone a significant period of growth in the 1990s, with many new areas of research and development being explored.
Author : Giorgia Lupi
Publisher : Chronicle Books
ISBN 13 : 1616895462
Total Pages : 304 pages
Book Rating : 4.6/5 (168 download)
Download or read book Dear Data written by Giorgia Lupi and published by Chronicle Books. This book was released on 2016-09-13 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates "the infinitesimal, incomplete, imperfect, yet exquisitely human details of life," in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere.
Author : Katharine G. Abraham
Publisher : University of Chicago Press
ISBN 13 : 022680125X
Total Pages : 502 pages
Book Rating : 4.2/5 (268 download)
Download or read book Big Data for Twenty-First-Century Economic Statistics written by Katharine G. Abraham and published by University of Chicago Press. This book was released on 2022-03-11 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction.Big data for twenty-first-century economic statistics: the future is now /Katharine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro --Toward comprehensive use of big data in economic statistics.Reengineering key national economic indicators /Gabriel Ehrlich, John Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro ;Big data in the US consumer price index: experiences and plans /Crystal G. Konny, Brendan K. Williams, and David M. Friedman ;Improving retail trade data products using alternative data sources /Rebecca J. Hutchinson ;From transaction data to economic statistics: constructing real-time, high-frequency, geographic measures of consumer spending /Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm ;Improving the accuracy of economic measurement with multiple data sources: the case of payroll employment data /Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz --Uses of big data for classification.Transforming naturally occurring text data into economic statistics: the case of online job vacancy postings /Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood ;Automating response evaluation for franchising questions on the 2017 economic census /Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer ;Using public data to generate industrial classification codes /John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts --Uses of big data for sectoral measurement.Nowcasting the local economy: using Yelp data to measure economic activity /Edward L. Glaeser, Hyunjin Kim, and Michael Luca ;Unit values for import and export price indexes: a proof of concept /Don A. Fast and Susan E. Fleck ;Quantifying productivity growth in the delivery of important episodes of care within the Medicare program using insurance claims and administrative data /John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood ;Valuing housing services in the era of big data: a user cost approach leveraging Zillow microdata /Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland --Methodological challenges and advances.Off to the races: a comparison of machine learning and alternative data for predicting economic indicators /Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch ;A machine learning analysis of seasonal and cyclical sales in weekly scanner data /Rishab Guha and Serena Ng ;Estimating the benefits of new products /W. Erwin Diewert and Robert C. Feenstra.
Author : Hyebin Song
Publisher :
ISBN 13 :
Total Pages : 213 pages
Book Rating : 4.:/5 (115 download)
Download or read book Large-scale Learning with Imperfect Data with Applications to Protein Science written by Hyebin Song and published by . This book was released on 2020 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis addresses inference problems with imperfect data-data presenting only partial information-in settings where the number of features p can grow with the number of examples n, potentially at a faster speed. Chapter 2 of this dissertation addresses a sparse estimation problem with large-scale Positive-Unlabeled data in which examples are either known to be positively labeled or have unknown responses. This Positive-Unlabeled learning, which is a type of semi-supervised learning, can be viewed as learning with partially missing responses. Using techniques in high-dimensional statistics and non-convex optimization, I propose a novel, scalable algorithm with the optimal mean-square error guarantee. Chapter 3 addresses a more general problem of learning with noisy labels, which includes positive-unlabeled data as a particular case. With a key observation that a convex objective can be constructed based on the method-of-moments approach, I present results for two estimators based on the convex and non-convex approaches. Both point estimation and testing problems are addressed in classical and high-dimensional regimes, where we provide mean-squared error guarantees for the two estimators together with valid testing procedures based on the de-biasing of the estimates. Deep Mutational Scanning (DMS) is a recently developed method in proteomics that uses high-throughput screening and next-generation sequencing technology to generate millions of functional sequences and sequences with unknown functionalities. In Chapter 4, I present a new framework-developed based on my aforementioned works in the high-dimensional PU learning or noisy labels problem-for protein modeling with DMS data. I demonstrate the success of the proposed framework in prediction and protein engineering.
Author : Vladimir Cherkassky
Publisher : John Wiley & Sons
ISBN 13 : 9780470140512
Total Pages : 560 pages
Book Rating : 4.1/5 (45 download)
Download or read book Learning from Data written by Vladimir Cherkassky and published by John Wiley & Sons. This book was released on 2007-09-10 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
Author : Lawrence A. Birnbaum
Publisher : Morgan Kaufmann
ISBN 13 : 1483298175
Total Pages : 682 pages
Book Rating : 4.4/5 (832 download)
Download or read book Machine Learning Proceedings 1991 written by Lawrence A. Birnbaum and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning
