Scaling Up Machine Learning
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Scaling Up Machine Learning
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Author : Ron Bekkerman
language : en
Publisher: Cambridge University Press
Release Date : 2011-12-30
Scaling Up Machine Learning written by Ron Bekkerman 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 2011-12-30 with Computers categories.
This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms, and deep dives into several applications, make the book equally useful for researchers, students and practitioners.
Scaling Up Machine Learning
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Author : Ron Bekkerman
language : en
Publisher: Cambridge University Press
Release Date : 2012
Scaling Up Machine Learning written by Ron Bekkerman 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 2012 with Computers categories.
This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
Issues In Scaling Up Machine Learning
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Author :
language : en
Publisher:
Release Date : 1997
Issues In Scaling Up Machine Learning 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.
This grant investigates issues in improving the accuracy of machine learning systems. The classic machine learning paradigm for prediction has been to learn a set of decision structures or models from a training set and select one for prediction on unseen test data. Rather than select a single node from the set, the focus of this project's research has been to combine the prediction of the learned models to form an improved estimate. The two fronts of this research are regression and classification. In the realm of regression, the task is to predict a single continuous value for an example. The majority of research in this area has focused on simple linear combination of the learned models. The nature of these weights may span from being highly regularized completely unconstrained. A set of weights is considered highly regularized if they are all positive, they sum to one, or they are uniform. Completely unconstrained weights have no restrictions and may be derived by methods like ordinary least squares regression. The degree of regularization required depends on the particular regression problem. The project has developed a technique called PCRY, which automatically estimates the appropriate degrease regularization for a given data set. The basic idea is to use the eigen structure of the model predictions on the training data to derive a continuum of possible weight sets ranging front highly regularized to completely unconstrained. Cross validation is used to estimate which weight set is most appropriate.
Proceedings Of The Thirteenth National Conference On Artificial Intelligence And The Eighth Innovative Applications Of Artificial Intelligence Conference
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Author : American Association for Artificial Intelligence
language : en
Publisher:
Release Date : 1996
Proceedings Of The Thirteenth National Conference On Artificial Intelligence And The Eighth Innovative Applications Of Artificial Intelligence Conference written by American Association for Artificial Intelligence and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
AAAI proceedings describe innovative concepts, techniques, perspectives, and observations that present promising research directions in artificial intelligence. August 4-8, 1996, Portland, OregonAAAI '96 provides a broad forum for information exchange and interaction among researchers working in different subdisciplines, in different research paradigms, and in different stages of research in artificial intelligence. Topics cover principles underlying cognition, perception and action; design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analysis of tasks and domains in which intelligent systems perform. Included are contributions that describe theoretical, empirical, or experimental results; represent areas of AI that may have been underrepresented in recent conferences; present promising new research concepts, techniques, or perspectives; or discuss issues that cross traditional subdisciplinary boundaries. Two-volume setDistributed for the AAAI Press
Machine Learning
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Author : Lorenza Saitta
language : en
Publisher: Morgan Kaufmann Publishers
Release Date : 1996
Machine Learning written by Lorenza Saitta and has been published by Morgan Kaufmann Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
Advances In Distributed And Parallel Knowledge Discovery
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Author : Hillol Kargupta
language : en
Publisher: AAAI Press
Release Date : 2000
Advances In Distributed And Parallel Knowledge Discovery written by Hillol Kargupta and has been published by AAAI Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Computers categories.
This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques. Foreword by Vipin Kumar Knowledge discovery and data mining (KDD) deals with the problem of extracting interesting associations, classifiers, clusters, and other patterns from data. The emergence of network-based distributed computing environments has introduced an important new dimension to this problem--distributed sources of data. Traditional centralized KDD typically requires central aggregation of distributed data, which may not always be feasible because of limited network bandwidth, security concerns, scalability problems, and other practical issues. Distributed knowledge discovery (DKD) works with the merger of communication and computation by analyzing data in a distributed fashion. This technology is particularly useful for large heterogeneous distributed environments such as the Internet, intranets, mobile computing environments, and sensor-networks.When the data sets are large, scaling up the speed of the KDD process is crucial. Parallel knowledge discovery (PKD) techniques addresses this problem by using high-performance multiprocessor machines. This book presents introductions to DKD and PKD, extensive reviews of the field, and state-of-the-art techniques. Contributors Rakesh Agrawal, Khaled AlSabti, Stuart Bailey, Philip Chan, David Cheung, Vincent Cho, Joydeep Ghosh, Robert Grossman, Yi-ke Guo, John Hale, John Hall, Daryl Hershberger, Ching-Tien Ho, Erik Johnson, Chris Jones, Chandrika Kamath, Hillol Kargupta, Charles Lo, Balinder Malhi, Ron Musick, Vincent Ng, Byung-Hoon Park, Srinivasan Parthasarathy, Andreas Prodromidis, Foster Provost, Jian Pun, Ashok Ramu, Sanjay Ranka, Mahesh Sreenivas, Salvatore Stolfo, Ramesh Subramonian, Janjao Sutiwaraphun, Kagan Tummer, Andrei Turinsky, Beat Wüthrich, Mohammed Zaki, Joshua Zhang
Scaling Up Reinforcement Learning Without Sacrificing Optimality By Constraining Exploration
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Author : Timothy Arthur Mann
language : en
Publisher:
Release Date : 2013
Scaling Up Reinforcement Learning Without Sacrificing Optimality By Constraining Exploration written by Timothy Arthur Mann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
The purpose of this dissertation is to understand how algorithms can efficiently learn to solve new tasks based on previous experience, instead of being explicitly programmed with a solution for each task that we want it to solve. Here a task is a series of decisions, such as a robot vacuum deciding which room to clean next or an intelligent car deciding to stop at a traffic light. In such a case, state-of-the-art learning algorithms are difficult to employ in practice because they often make thou- sands of mistakes before reliably solving a task. However, humans learn solutions to novel tasks, often making fewer mistakes, which suggests that efficient learning algorithms may exist. One advantage that humans have over state- of-the-art learning algorithms is that, while learning a new task, humans can apply knowledge gained from previously solved tasks. The central hypothesis investigated by this dissertation is that learning algorithms can solve new tasks more efficiently when they take into consideration knowledge learned from solving previous tasks. Al- though this hypothesis may appear to be obviously true, what knowledge to use and how to apply that knowledge to new tasks is a challenging, open research problem. I investigate this hypothesis in three ways. First, I developed a new learning algorithm that is able to use prior knowledge to constrain the exploration space. Second, I extended a powerful theoretical framework in machine learning, called Probably Approximately Correct, so that I can formally compare the efficiency of algorithms that solve only a single task to algorithms that consider knowledge from previously solved tasks. With this framework, I found sufficient conditions for using knowledge from previous tasks to improve efficiency of learning to solve new tasks and also identified conditions where transferring knowledge may impede learning. I present situations where transfer learning can be used to intelligently constrain the exploration space so that optimality loss can be minimized. Finally, I tested the efficiency of my algorithms in various experimental domains. These theoretical and empirical results provide support for my central hypothesis. The theory and experiments of this dissertation provide a deeper understanding of what makes a learning algorithm efficient so that it can be widely used in practice. Finally, these results also contribute the general goal of creating autonomous machines that can be reliably employed to solve complex tasks. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148402
Scaling Machine Learning With Spark
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Author : Adi Polak
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-03-07
Scaling Machine Learning With Spark written by Adi Polak 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 2023-03-07 with Computers categories.
Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better. Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology. You will: Explore machine learning, including distributed computing concepts and terminology Manage the ML lifecycle with MLflow Ingest data and perform basic preprocessing with Spark Explore feature engineering, and use Spark to extract features Train a model with MLlib and build a pipeline to reproduce it Build a data system to combine the power of Spark with deep learning Get a step-by-step example of working with distributed TensorFlow Use PyTorch to scale machine learning and its internal architecture
Machine Learning Ecml
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Author :
language : en
Publisher:
Release Date : 1998
Machine Learning Ecml written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Induction (Logic) categories.
Machine Learning Ecml 98
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Author : Claire Nedellec
language : en
Publisher: Lecture Notes in Artificial Intelligence
Release Date : 1998-04-08
Machine Learning Ecml 98 written by Claire Nedellec and has been published by Lecture Notes in Artificial Intelligence this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-04-08 with Computers categories.
This book constitutes the refereed proceedings of the 10th European Conference on Machine Learning, ECML-98, held in Chemnitz, Germany, in April 1998. The book presents 21 revised full papers and 25 short papers reporting on work in progress together with two invited contributions; the papers were selected from a total of 100 submissions. The book is divided in sections on applications of ML, Bayesian networks, feature selection, decision trees, support vector learning, multiple models for classification, inductive logic programming, relational learning, instance-based learning, clustering, genetic algorithms, reinforcement learning and neural networks.