Surrogate Data Models Interpreting Large Scale Machine Learning Crisis Prediction Models
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Surrogate Data Models Interpreting Large Scale Machine Learning Crisis Prediction Models
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Author : Jorge Chan-Lau
language : en
Publisher: International Monetary Fund
Release Date : 2023-02-24
Surrogate Data Models Interpreting Large Scale Machine Learning Crisis Prediction Models written by Jorge Chan-Lau and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-24 with Business & Economics categories.
Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies. This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models. The appropriateness of this approach is assessed by their application to large-scale crisis prediction models developed at the IMF. The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.
Surrogate Data Models
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Author : Jorge Chan-Lau
language : en
Publisher:
Release Date : 2023
Surrogate Data Models written by Jorge Chan-Lau and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Data Science For Economics And Finance
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Author : Sergio Consoli
language : en
Publisher: Springer Nature
Release Date : 2021-06-09
Data Science For Economics And Finance written by Sergio Consoli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-09 with Computers categories.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
Neural Networks In Business
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Author : Kate A. Smith
language : en
Publisher: IGI Global
Release Date : 2003-01-01
Neural Networks In Business written by Kate A. Smith and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-01 with Computers categories.
"For professionals, students, and academics interested in applying neural networks to a variety of business applications, this reference book introduces the three most common neural network models and how they work. A wide range of business applications and a series of global case studies are presented to illustrate the neural network models provided. Each model or technique is discussed in detail and used to solve a business problem such as managing direct marketing, calculating foreign exchange rates, and improving cash flow forecasting."
Machine Learning Techniques In Economics
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Author : Atin Basuchoudhary
language : en
Publisher: Springer
Release Date : 2017-12-28
Machine Learning Techniques In Economics written by Atin Basuchoudhary and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-28 with Business & Economics categories.
This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.
Convex Optimization
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Author : Stephen P. Boyd
language : en
Publisher: Cambridge University Press
Release Date : 2004-03-08
Convex Optimization written by Stephen P. Boyd and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-03-08 with Business & Economics categories.
Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
Patterns Predictions And Actions
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Author : Moritz Hardt
language : en
Publisher: Princeton University Press
Release Date : 2022-08-23
Patterns Predictions And Actions written by Moritz Hardt and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-23 with Computers categories.
An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
Statistical Models
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Author : David A. Freedman
language : en
Publisher: Cambridge University Press
Release Date : 2009-04-27
Statistical Models written by David A. Freedman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-27 with Mathematics categories.
This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
Neural Network Methods For Natural Language Processing
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Author : Yoav Goldberg
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Neural Network Methods For Natural Language Processing written by Yoav Goldberg and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Neural Approaches To Conversational Ai Question Answering Task Oriented Dialogues And Social Chatbots
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Author : Jianfeng Gao
language : en
Publisher: Foundations and Trends(r) in I
Release Date : 2019-02-21
Neural Approaches To Conversational Ai Question Answering Task Oriented Dialogues And Social Chatbots written by Jianfeng Gao and has been published by Foundations and Trends(r) in I this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Computers categories.
This monograph is the first survey of neural approaches to conversational AI that targets Natural Language Processing and Information Retrieval audiences. It provides a comprehensive survey of the neural approaches to conversational AI that have been developed in the last few years, covering QA, task-oriented and social bots with a unified view of optimal decision making.The authors draw connections between modern neural approaches and traditional approaches, allowing readers to better understand why and how the research has evolved and to shed light on how they can move forward. They also present state-of-the-art approaches to training dialogue agents using both supervised and reinforcement learning. Finally, the authors sketch out the landscape of conversational systems developed in the research community and released in industry, demonstrating via case studies the progress that has been made and the challenges that are still being faced.Neural Approaches to Conversational AI is a valuable resource for students, researchers, and software developers. It provides a unified view, as well as a detailed presentation of the important ideas and insights needed to understand and create modern dialogue agents that will be instrumental to making world knowledge and services accessible to millions of users in ways that seem natural and intuitive.