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Selective Algorithms For Large Scale Classification And Structured Learning
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Book Synopsis Selective Algorithms for Large-scale Classification and Structured Learning by :
Download or read book Selective Algorithms for Large-scale Classification and Structured Learning written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning Strategies for Large-scale Taxonomies by : Rohit Babbar
Download or read book Machine Learning Strategies for Large-scale Taxonomies written by Rohit Babbar and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the era of Big Data, we need efficient and scalable machine learning algorithms which can perform automatic classification of Tera-Bytes of data. In this thesis, we study the machine learning challenges for classification in large-scale taxonomies. These challenges include computational complexity of training and prediction and the performance on unseen data. In the first part of the thesis, we study the underlying power-law distribution in large-scale taxonomies. This analysis then motivates the derivation of bounds on space complexity of hierarchical classifiers. Exploiting the study of this distribution further, we then design classification scheme which leads to better accuracy on large-scale power-law distributed categories. We also propose an efficient method for model-selection when training multi-class version of classifiers such as Support Vector Machine and Logistic Regression. Finally, we address another key model selection problem in large scale classification concerning the choice between flat versus hierarchical classification from a learning theoretic aspect. The presented generalization error analysis provides an explanation to empirical findings in many recent studies in large-scale hierarchical classification. We further exploit the developed bounds to propose two methods for adapting the given taxonomy of categories to output taxonomies which yield better test accuracy when used in a top-down setup.
Book Synopsis Improved Classification Rates for Localized Algorithms under Margin Conditions by : Ingrid Karin Blaschzyk
Download or read book Improved Classification Rates for Localized Algorithms under Margin Conditions written by Ingrid Karin Blaschzyk and published by Springer Nature. This book was released on 2020-03-18 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance.
Book Synopsis Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIII by : Abdelkader Hameurlain
Download or read book Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIII written by Abdelkader Hameurlain and published by Springer Nature. This book was released on 2020-08-12 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 43rd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five revised selected regular papers. Topics covered include classification tasks, machine learning algorithms, top-k queries, business process redesign and a knowledge capitalization framework.
Book Synopsis Large Scale Hierarchical Classification: State of the Art by : Azad Naik
Download or read book Large Scale Hierarchical Classification: State of the Art written by Azad Naik and published by Springer. This book was released on 2018-10-09 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the authors provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as: 1. High imbalance between classes at different levels of the hierarchy 2. Incorporating relationships during model learning leads to optimization issues 3. Feature selection 4. Scalability due to large number of examples, features and classes 5. Hierarchical inconsistencies 6. Error propagation due to multiple decisions involved in making predictions for top-down methods The brief also demonstrates how multiple hierarchies can be leveraged for improving the HC performance using different Multi-Task Learning (MTL) frameworks. The purpose of this book is two-fold: 1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques. 2. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC. New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.
Book Synopsis Efficient Algorithms for Structured Inference and Collaborative Learning by : Abolfazl Hashemi
Download or read book Efficient Algorithms for Structured Inference and Collaborative Learning written by Abolfazl Hashemi and published by . This book was released on 2020 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive amounts of data collected by modern information systems give rise to new challenges in the fields of signal processing, machine learning, and data analysis. In contemporary large-scale datasets, there are often hidden low-dimensional structures either in the form of parsimonious representations that best fit the data or the desired unknown information itself. Identifying parsimonious representations and exploiting underlying structural constraints lead to improved inference. Furthermore, these large-scale datasets are distributed among a network of resource-constrained systems capable of exchanging information. Hence, designing accelerated and communication efficient learning and inference algorithms is of critical importance. In the first part of this dissertation, we first study the setting where the unknown parameter of interest has hidden sparsity structures. The task of reconstructing the sparse parameter can be formulated as an l0-constrained least square problem. Motivated by the need for fast and accurate sparse recovery in large-scale setting, we propose two efficient sparse reconstruction and support selection algorithms and analyze their reconstruction performance in a variety of settings. Next, we consider applications of the proposed algorithms in structured data clustering problems where the high-dimensional data is a collection of points lying on a union of low-dimensional and evolving subspaces. By exploiting sparsity to model the low-dimensional union-of-subspaces structure of the data as well as its underlying evolutionary structure, we propose a novel evolutionary subspace clustering framework and demonstrate its successful deployment in computer vision and oceanography applications. In the second part of this dissertation, we consider observation selection and information gathering algorithms in communication-constrained networked systems where we study structural properties of observation selection criteria, design efficient greedy algorithms, and analyze their performance by leveraging the framework of weak submodular optimization. In the final part of this dissertation, we study the task of learning parameters of a machine learning model in a collaborative manner over a communication-constrained network, and design an efficient communication compressing optimization algorithm that reduces the amount of communication in the network while achieving a near optimal converge rate for general nonconvex learning tasks
Book Synopsis Review of Large-scale Coordinate Descent Algorithms for Multi-class Classification with Memory Constraints by : Aleksandar Jovanovich
Download or read book Review of Large-scale Coordinate Descent Algorithms for Multi-class Classification with Memory Constraints written by Aleksandar Jovanovich and published by . This book was released on 2013 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data can impact the performance of many standard measures used for classification. Specifically the efficiency of multi-class classification algorithms when the dataset is too large to fit into limited memory available needs to be explored. Different algorithms with varying complexity have been proposed in the literature. Two of the most recognized classification algorithms, batch learning and online learning have emerged as the most consistent options when solving for a multi-class problems. Presently a gap in the documentation of such algorithms exists in the literature available. Furthermore, the recent development of the online multi-class solver warrants a detailed examination. This thesis will address both concerns, providing detailed documentation of the analysis, comparisons of the algorithms, plots of the results, as well as a discussion about the findings.
Book Synopsis Sparse and Large-scale Learning Models and Algorithms for Mining Heterogeneous Big Data by : Xiao Cai
Download or read book Sparse and Large-scale Learning Models and Algorithms for Mining Heterogeneous Big Data written by Xiao Cai and published by . This book was released on 2014 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of PC, internet as well as mobile devices, we are facing a data exploding era. On one hand, more and more features can be collected to describe the data, making the size of the data descriptor larger and larger. On the other hand, the number of data itself explodes and can be collected from multiple resources. When the data becomes large scale, the traditional data analysis method may fail, suffering the curse of dimensionality and etc. In order to explore and analyze the large-scale data more accurately and more efficiently, based on the characteristic of the data, we propose several learning algorithms to mine the Heterogeneous data. To be specific, if the feature dimension is large, we propose several sparse learning based feature selection methods to select the key words from the text or to find the bio-marker from the gene expression data; if the number of data itself is huge, we proposed multi-view K-Means method to do the clustering to avoid the heavy graph construction burden; if the data is represented or collected by multiple resources, we propose graph based multi-modality model to do semi-supervised learning and clustering. In addition, if the number of classes is large, we provides a global solution to the low-rank regression and proves that the low-rank regression is equivalent to doing linear regression in LDA space. We empirically evaluate each of our proposed models on several benchmark data sets and our methods can consistently achieve superior results with the comparison of state-of-art methods.
Book Synopsis Selective Sampling for Classification by : Sara Shanian
Download or read book Selective Sampling for Classification written by Sara Shanian and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mining Complex Data by : Djamel A. Zighed
Download or read book Mining Complex Data written by Djamel A. Zighed and published by Springer. This book was released on 2008-10-10 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.
Book Synopsis Intelligent Data Engineering and Automated Learning – IDEAL 2019 by : Hujun Yin
Download or read book Intelligent Data Engineering and Automated Learning – IDEAL 2019 written by Hujun Yin and published by Springer Nature. This book was released on 2019-11-07 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.
Book Synopsis Scientific and Technical Aerospace Reports by :
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 994 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Big Data Analytics by : Yong Shi
Download or read book Advances in Big Data Analytics written by Yong Shi and published by Springer Nature. This book was released on 2022-01-13 with total page 733 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence. /divSince each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.
Book Synopsis Geometry and Vision by : Minh Nguyen
Download or read book Geometry and Vision written by Minh Nguyen and published by Springer Nature. This book was released on 2021-03-17 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected papers from the First International Symposium on Geometry and Vision, ISGV 2021, held in Auckland, New Zealand, in January 2021. Due to the COVID-19 pandemic the conference was held in partially virtual format. The 29 papers were thoroughly reviewed and selected from 50 submissions. They cover topics in areas of digital geometry, graphics, image and video technologies, computer vision, and multimedia technologies.
Book Synopsis Algorithms in Bioinformatics by : Roderic Guigo
Download or read book Algorithms in Bioinformatics written by Roderic Guigo and published by Springer. This book was released on 2003-06-30 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are pleased to present the proceedings of the Second Workshop on Al- rithms in Bioinformatics (WABI 2002), which took place on September 17-21, 2002 in Rome, Italy. The WABI workshop was part of a three-conference me- ing, which, in addition to WABI, included the ESA and APPROX 2002. The three conferences are jointly called ALGO 2002, and were hosted by the F- ulty of Engineering, University of Rome “La Sapienza”. Seehttp://www.dis. uniroma1.it/ ̃algo02 for more details. The Workshop on Algorithms in Bioinformatics covers research in all areas of algorithmic work in bioinformatics and computational biology. The emphasis is on discrete algorithms that address important problems in molecular biology, genomics,andgenetics,thatarefoundedonsoundmodels,thatarecomputati- ally e?cient, and that have been implemented and tested in simulations and on real datasets. The goal is to present recent research results, including signi?cant work in progress, and to identify and explore directions of future research. Original research papers (including signi?cant work in progress) or sta- of-the-art surveys were solicited on all aspects of algorithms in bioinformatics, including, but not limited to: exact and approximate algorithms for genomics, genetics, sequence analysis, gene and signal recognition, alignment, molecular evolution, phylogenetics, structure determination or prediction, gene expression and gene networks, proteomics, functional genomics, and drug design.
Book Synopsis Open Set Classification for Deep Learning in Large-scale and Continual Learning Models by : Ryne Roady
Download or read book Open Set Classification for Deep Learning in Large-scale and Continual Learning Models written by Ryne Roady and published by . This book was released on 2020 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers require the ability to recognize inputs from outside the training set as unknowns and update representations in near real-time to account for novel concepts unknown during offline training. This problem has been studied under multiple paradigms including out-of-distribution detection and open set recognition; however, for convolutional neural networks, there have been two major approaches: 1) inference methods to separate known inputs from unknown inputs and 2) feature space regularization strategies to improve model robustness to novel inputs. In this dissertation, we explore the relationship between the two approaches and directly compare performance on large-scale datasets that have more than a few dozen categories. Using the ImageNet large-scale classification dataset, we identify novel combinations of regularization and specialized inference methods that perform best across multiple open set classification problems of increasing difficulty level. We find that input perturbation and temperature scaling yield significantly better performance on large-scale datasets than other inference methods tested, regardless of the feature space regularization strategy. Conversely, we also find that improving performance with advanced regularization schemes during training yields better performance when baseline inference techniques are used; however, this often requires supplementing the training data with additional background samples which is difficult in large-scale problems. To overcome this problem we further propose a simple regularization technique that can be easily applied to existing convolutional neural network architectures that improves open set robustness without the requirement for a background dataset. Our novel method achieves state-of-the-art results on open set classification baselines and easily scales to large-scale problems. Finally, we explore the intersection of open set and continual learning to establish baselines for the first time for novelty detection while learning from online data streams. To accomplish this we establish a novel dataset created for evaluating image open set classification capabilities of streaming learning algorithms. Finally, using our new baselines we draw conclusions as to what the most computationally efficient means of detecting novelty in pre-trained models and what properties of an efficient open set learning algorithm operating in the streaming paradigm should possess."--Abstract.
Book Synopsis Data-Driven Computational Neuroscience by : Concha Bielza
Download or read book Data-Driven Computational Neuroscience written by Concha Bielza and published by Cambridge University Press. This book was released on 2020-11-26 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.