Boosted Statistical Relational Learners PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Boosted Statistical Relational Learners PDF full book. Access full book title Boosted Statistical Relational Learners.
| Author | : Sriraam Natarajan |
| Publisher | : Springer |
| Total Pages | : 79 |
| Release | : 2015-03-03 |
| Genre | : Computers |
| ISBN | : 3319136445 |
Download Boosted Statistical Relational Learners Book in PDF, ePub and Kindle
This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.
| Author | : Lise Getoor |
| Publisher | : MIT Press |
| Total Pages | : 602 |
| Release | : 2019-09-22 |
| Genre | : Computers |
| ISBN | : 0262538687 |
Download Introduction to Statistical Relational Learning Book in PDF, ePub and Kindle
Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
| Author | : |
| Publisher | : |
| Total Pages | : 198 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
Download Efficient Learning of Statistical Relational Models Book in PDF, ePub and Kindle
Machine Learning has been successfully applied to many prediction problems in varying domains. But standard techniques assume that the examples are independent of each other and have the same number of features. In many domains, the objects can be inter-related and have different number of features. To build probabilistic models over such data, Statistical Relational Learning (SRL) methods have been proposed, which combine first-order logic representation with probabilities. But due to their high expressivity, learning the structure of SRL models can be computationally intensive. I present a structure-learning approach that learns multiple weak rules of thumb via functional-gradient boosting. My approach can be used to learn the structure of two popular SRL models. I empirically demonstrate it to be more accurate and computationally faster than state-of-the-art methods. To further increase the applicability of my approach, I extend it to handle missing data by deriving an Expectation-Maximization approach for relational models. To handle Natural Language Processing domains with only positive labeled examples, I present and evaluate a non-parametric approach for relational one-class classification using a tree-based relational distance measure. Apart from learning models, this thesis also explores knowledge representation in Markov Logic Networks (MLN). I present and evaluate an approach that can convert multi-level combination functions along with their corresponding parameters into MLN clauses. I present an algorithm for converting two combination functions into MLNs and show the correctness of my transformation. Finally this thesis shows how my approach can be used for Alzhiemer's disease prediction from MRI images as well as to augment expert rules for temporal relation extraction. I present my approach for a large-scale novel relation extraction task, where I process terabytes of streaming data to detect changes in extracted relations. Overall, this thesis presents multiple structure-learning approaches for SRL, starting from a boosting-based algorithm, which is extended to handle missing values via EM. Next, I present a structure-learning approach for one-class classification by learning a relational distance metric. I present application of these structure-learning approach on multiple SRL datasets and real-world tasks.
| Author | : Guy Van den Broeck |
| Publisher | : MIT Press |
| Total Pages | : 455 |
| Release | : 2021-08-17 |
| Genre | : Computers |
| ISBN | : 0262366185 |
Download An Introduction to Lifted Probabilistic Inference Book in PDF, ePub and Kindle
Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.
| Author | : Gautam Kunapuli |
| Publisher | : Simon and Schuster |
| Total Pages | : 350 |
| Release | : 2023-05-30 |
| Genre | : Computers |
| ISBN | : 163835670X |
Download Ensemble Methods for Machine Learning Book in PDF, ePub and Kindle
Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering and ensemble diversity Interpretability and explainability for ensemble methods Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. About the Technology Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. About the Book Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation! What’s Inside Bagging, boosting, and gradient boosting Methods for classification, regression, and retrieval Interpretability and explainability for ensemble methods Feature engineering and ensemble diversity About the Reader For Python programmers with machine learning experience. About the Author Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. Table of Contents PART 1 - THE BASICS OF ENSEMBLES 1 Ensemble methods: Hype or hallelujah? PART 2 - ESSENTIAL ENSEMBLE METHODS 2 Homogeneous parallel ensembles: Bagging and random forests 3 Heterogeneous parallel ensembles: Combining strong learners 4 Sequential ensembles: Adaptive boosting 5 Sequential ensembles: Gradient boosting 6 Sequential ensembles: Newton boosting PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA 7 Learning with continuous and count labels 8 Learning with categorical features 9 Explaining your ensembles
| Author | : Jörg Lässig |
| Publisher | : Springer |
| Total Pages | : 277 |
| Release | : 2016-04-20 |
| Genre | : Technology & Engineering |
| ISBN | : 3319318586 |
Download Computational Sustainability Book in PDF, ePub and Kindle
The book at hand gives an overview of the state of the art research in Computational Sustainability as well as case studies of different application scenarios. This covers topics such as renewable energy supply, energy storage and e-mobility, efficiency in data centers and networks, sustainable food and water supply, sustainable health, industrial production and quality, etc. The book describes computational methods and possible application scenarios.
| Author | : Peter A. Flach |
| Publisher | : Springer |
| Total Pages | : 904 |
| Release | : 2012-09-08 |
| Genre | : Computers |
| ISBN | : 3642334601 |
Download Machine Learning and Knowledge Discovery in Databases Book in PDF, ePub and Kindle
This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.
| Author | : Hassan Khosravi |
| Publisher | : |
| Total Pages | : 250 |
| Release | : 2012 |
| Genre | : Machine learning |
| ISBN | : |
Download Directed Models for Statistical Relational Learning Book in PDF, ePub and Kindle
Statistical Relational Learning is a new branch of machine learning that aims to model a joint distribution over relational data. Relational data consists of different types of objects where each object is characterized with a different set of attributes. The structure of relational data presents an opportunity for objects to carry additional information via their links and enables the model to show correlations among objects and their relationships. This dissertation focuses on learning graphical models for such data. Learning graphical models for relational data is much more challenging than learning graphical models for propositional data. One of the challenges of learning graphical models for relational data is that relational data, unlike propositional data, is non independent and identically distributed and cannot be viewed in a single table. Relational data can be modeled using a graph, where objects are the nodes and relationships between the objects are the edges. In this graph, there may be multiple edges between two nodes because objects may have different types of relationships with each other. The existence of multiple paths of different length among objects makes the learning procedure much harder than learning from a single table. We use a lattice search approach with lifted learning to deal with the multiple path problem. We focus on learning the structure of Markov Logic Networks, which are a first order extension of Markov Random Fields. Markov Logic Networks are a prominent undirected statical relational model that have achieved impressive performance on a variety of statistical relational learning tasks. Our approach combines the scalability and efficiency of learning in directed relational models, and the inference power and theoretical foundations of undirected relational models. We utilize an extension of Bayesian networks based on first order logic for learning class-level or first-order dependencies, which model the general database statistics over attributes of linked objects and their links. We then convert this model to a Markov Logic Network using the standard moralization procedure. Experimental results indicate that our methods are two orders of magnitude faster than, and predictive metrics are superior or competitive with, state-of-the-art Markov Logic Network learners.
| Author | : Gerson Zaverucha |
| Publisher | : Springer |
| Total Pages | : 152 |
| Release | : 2014-09-23 |
| Genre | : Mathematics |
| ISBN | : 3662449234 |
Download Inductive Logic Programming Book in PDF, ePub and Kindle
This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.
| Author | : Nicolas Lachiche |
| Publisher | : Springer |
| Total Pages | : 185 |
| Release | : 2018-03-19 |
| Genre | : Mathematics |
| ISBN | : 3319780905 |
Download Inductive Logic Programming Book in PDF, ePub and Kindle
This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, France, in September 2017. The 12 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
