Conferences related to Machine Learning

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2019 IEEE Power & Energy Society General Meeting (PESGM)

The Annual IEEE PES General Meeting will bring together over 2900 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more


ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world.


2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Industrial Informatics, Computational Intelligence, Control and Systems, Cyber-physicalSystems, Energy and Environment, Mechatronics, Power Electronics, Signal and InformationProcessing, Network and Communication Technologies


2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

IEEE CCNC 2018 will present the latest developments and technical solutions in the areas of home networking, consumer networking, enabling technologies (such as middleware) and novel applications and services. The conference will include a peer-reviewed program of technical sessions, special sessions, business application sessions, tutorials, and demonstration sessions


2018 24th International Conference on Pattern Recognition (ICPR)

ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields

  • 2016 23rd International Conference on Pattern Recognition (ICPR)

    ICPR'2016 will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields.

  • 2014 22nd International Conference on Pattern Recognition (ICPR)

    ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.

  • 2012 21st International Conference on Pattern Recognition (ICPR)

    ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.

  • 2002 16th International Conference on Pattern Recognition


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Periodicals related to Machine Learning

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on

Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.


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Most published Xplore authors for Machine Learning

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Xplore Articles related to Machine Learning

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A user-centric machine learning framework for cyber security operations center

[{u'author_order': 1, u'affiliation': u'ZhongDu Technologies, Inc., Shaoxing, Zhejiang, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37086150803', u'full_name': u'Charles Feng', u'id': 37086150803}, {u'author_order': 2, u'affiliation': u'Center for Advanced Machine Learning, Symantec Corporation, Mountain View, California, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37086050813', u'full_name': u'Shuning Wu', u'id': 37086050813}, {u'author_order': 3, u'affiliation': u'Norton Business Unit, Symantec Corporation, Mountain View, California, USA', u'authorUrl': u'https://ieeexplore.ieee.org/author/37086050485', u'full_name': u'Ningwei Liu', u'id': 37086050485}] 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), 2017

To assure cyber security of an enterprise, typically SIEM (Security Information and Event Management) system is in place to normalize security events from different preventive technologies and flag alerts. Analysts in the security operation center (SOC) investigate the alerts to decide if it is truly malicious or not. However, generally the number of alerts is overwhelming with majority of them ...


Comparison of Classification Techniques used in Machine Learning as Applied on Vocational Guidance Data

[{u'author_order': 1, u'authorUrl': u'https://ieeexplore.ieee.org/author/37856895400', u'full_name': u'Halil Ibrahim Bulbul', u'id': 37856895400}, {u'author_order': 2, u'authorUrl': u'https://ieeexplore.ieee.org/author/37842715100', u'full_name': u'\xd6zkan Unsal', u'id': 37842715100}] 2011 10th International Conference on Machine Learning and Applications and Workshops, 2011

Recent developments in information systems as well as computerization of business processes by organizations have led to a faster, easier and more accurate data analysis. Data mining and machine learning techniques have been used increasingly in the analysis of data in various fields ranging from medicine to finance, education and energy applications. Machine learning techniques make it possible to deduct ...


Statistical Machine Learning Used in Integrated Anti-Spam System

[{u'author_order': 1, u'affiliation': u'CISTR, Beijing University of Posts & Telecommunications, Beijing 100876, P.R.CHINA. EMAIL: zpf@126.com', u'authorUrl': u'https://ieeexplore.ieee.org/author/37829856600', u'full_name': u'Peng-Fei Zhang', u'id': 37829856600}, {u'author_order': 2, u'affiliation': u'Information Network Center, Beijing University of Posts & Telecommunications, Beijing 100876, P.R.CHINA. EMAIL: suyj@bupt.edu.cn', u'authorUrl': u'https://ieeexplore.ieee.org/author/37854609900', u'full_name': u'Yu-Jie Su', u'id': 37854609900}, {u'author_order': 3, u'affiliation': u'CISTR, Beijing University of Posts & Telecommunications, Beijing 100876, P.R.CHINA. EMAIL: wangc@bt-t.com', u'authorUrl': u'https://ieeexplore.ieee.org/author/37292502000', u'full_name': u'Cong Wang', u'id': 37292502000}] 2007 International Conference on Machine Learning and Cybernetics, 2007

IASS is the integrated anti-spam system, which adopts machine learning to filter spam in a intelligent, flexible, precise, and self-adaptive way. The methods of linear classification based on optimal separating hyperplane and K-means clustering are used in action recognition layer. The method of improved naive Bayes is used in content analysis layer. The application of machine learning helps improve the ...


The application of machine learning algorithm in underwriting process

[{u'author_order': 1, u'affiliation': u'Sch. of Math. Sci., South China Univ. of Technol., Guangzhou, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37554999700', u'full_name': u'Yi Tan', u'id': 37554999700}, {u'author_order': 2, u'affiliation': u'Sch. of Math. Sci., South China Univ. of Technol., Guangzhou, China', u'authorUrl': u'https://ieeexplore.ieee.org/author/37069457600', u'full_name': u'Guo-Ji Zhang', u'id': 37069457600}] 2005 International Conference on Machine Learning and Cybernetics, 2005

This paper firstly analyses the actual underwriting methods of Chinese life insurance companies, and points out the merits and shortcomings of these methods. Then the incomplete database of insurance company is mined by the data mining's association rule algorithm. Thirdly the support vector machine (SVM) is applied to the underwriting process to classify the applicants. Finally the directions for improving ...


Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation

[{u'author_order': 1, u'affiliation': u'Institute of Communications Technology, Leibniz Universit\xe4t Hannover', u'authorUrl': u'https://ieeexplore.ieee.org/author/37085811990', u'full_name': u'Javier Conte Alcaraz', u'id': 37085811990}, {u'author_order': 2, u'affiliation': u'Institute of Communications Technology, Leibniz Universit\xe4t Hannover', u'authorUrl': u'https://ieeexplore.ieee.org/author/37946814200', u'full_name': u'Sanam Moghaddamnia', u'id': 37946814200}, {u'author_order': 3, u'affiliation': u'Institute of Communications Technology, Leibniz Universit\xe4t Hannover', u'authorUrl': u'https://ieeexplore.ieee.org/author/37086235951', u'full_name': u'Nils Poschadel', u'id': 37086235951}, {u'author_order': 4, u'affiliation': u'Institute of Communications Technology, Leibniz Universit\xe4t Hannover', u'authorUrl': u'https://ieeexplore.ieee.org/author/38526253800', u'full_name': u'J\xfcrgen Peissig', u'id': 38526253800}] 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), 2018

A novel real-time acoustic feedback (RTAF) based on machine learning to reduce the duration and to improve the progress in the rehabilitation is presented. Wearable technology (WT) has emerged as a viable means to provide low-cost digital healthcare and therapy course outside the medical environment like hospitals and clinics. In this paper we show that the RTAF together with WTs ...


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Educational Resources on Machine Learning

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eLearning

No eLearning Articles are currently tagged "Machine Learning"

IEEE.tv Videos

Machine Learning of Motor Skills for Robotics
Linear Regression: Intro to Machine Learning Workshop - IEEE Region 4 Presentation
Signal Processing and Machine Learning
ICASSP 2011 Trends in Machine Learning for Signal Processing
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
Large Scale Data Mining Using Genetics-Based Machine Learning 3
Cat and Mouse, Email Phishing and Machine Learning - Cybersecurity in a Hyperconnected World
Large Scale Data Mining Using Genetics-Based Machine Learning 1
Large Scale Data Mining Using Genetics-Based Machine Learning 2
Vladimir Vapnik accepts the IEEE John Von Neumann Medal - Honors Ceremony 2017
Big Data and Machine Learning in Cancer Genomics
Hardware-Software Co-Design for an Analog-Digital Accelerator for Machine Learning - Dejan Milojicic - ICRC 2018
Fog Computing Test Bed: Cutting Costs and Latency in Data Transmission - Fog World Congress
The Path to Robust Machine Learning: IEEE TechEthics Keynote with Richard Mallah
Geoffrey Hinton receives the IEEE/RSE James Clerk Maxwell Medal - Honors Ceremony 2016
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
Sensing and Decision Making in Social Networks
Q&A with Dillon Graham: IEEE Rebooting Computing Podcast, Episode 18
Fengrui Shi: Knowledge Co-creation in the OrganiCity: Data Annotation with JAMAiCA
Large-scale Neural Systems for Vision and Cognition

IEEE-USA E-Books

  • Non-convex Optimization for Machine Learning

    Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It introduces the rich literature in this area, as well as equips the reader with the tools and techniques needed to apply and analyze simple but powerful procedures for non-convex problems. Non-convex Optimization for Machine Learning is as self-contained as possible while not losing focus of the main topic of non-convex optimization techniques. The monograph initiates the discussion with entire chapters devoted to presenting a tutorial-like treatment of basic concepts in convex analysis and optimization, as well as their non-convex counterparts. The monograph concludes with a look at four interesting applications in the areas of machine learning and signal processing, and exploring how the non-convex optimization techniques introduced earlier can be used to solve these problems. The monograph also contains, for each of the topics discussed, exercises and figures designed to engage the reader, as well as extensive bibliographic notes pointing towards classical works and recent advances. Non-convex Optimization for Machine Learning can be used for a semester-length course on the basics of non-convex optimization with applications to machine learning. On the other hand, it is also possible to cherry pick individual portions, such the chapter on sparse recovery, or the EM algorithm, for inclusion in a broader course. Several courses such as those in machine learning, optimization, and signal processing may benefit from the inclusion of such topics.

  • Machine Learning: Fundamentals

    Prognostics and health management (PHM) facilitates maintenance decision‐making and provides usage feedback for the product design and validation process. Electronic component and product manufacturers need new ways to gain insights from the massive volume of data recently streaming in from their systems and sensors, and this can be accomplished by using machine learning (ML). This chapter provides the fundamentals of ML. ML algorithms can be divided into the following four categories depending on the amount and type of supervision they need while training: supervised, unsupervised, semi‐supervised, and reinforcement learning. ML algorithms can be classified into two different learning methods based on whether or not the algorithms can learn incrementally from a stream of incoming data: batch and online learning. Probability theory plays a significant role in ML, specifically as the design of learning algorithms often depends on probabilistic assumption of the data.

  • Machine Learning: Diagnostics and Prognostics

    Prognostics and health management (PHM) has emerged as an essential approach for preventing catastrophic failure and increasing system availability by reducing downtime, extending maintenance cycles, executing time repair actions, and lowering life‐cycle costs. This chapter provides a basic understanding of data‐driven diagnostics and prognostics. It reviews recent advancements of diagnosis and prognosis techniques with a focus on their applications in practice. The chapter discusses research opportunities that can lead to further improvement of PHM in both theory and practice. Bagging, also known as bootstrap aggregating, is an ensemble learning method that uses a series of homogeneous or heterogeneous machine learning algorithms to improve classification performance. Adaptive boosting is the first practical boosting algorithm and aims to convert a set of weak classifiers into a strong one sequentially. Prognostic techniques can be categorized into two groups: regression analysis and particle filtering.

  • Adversarial Machine Learning

    The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.

  • Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning

    This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. We show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The work presented here extends the original tensor voting framework with the addition of boundary inference capabilities; a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. We show complete analysis for some problems, while we briefly outline our approach for other applications and provide pointers to relevant sources.

  • MACHINE LEARNING FOR DATA STREAMS

    None

  • Machine Learning: Data Pre‐processing

    In prognostics and health management (PHM), data pre‐processing generally involves the following tasks: data cleansing, normalization, feature discovery, and imbalanced data management. Data cleansing is the process of detecting and correcting corrupt or inaccurate data. Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature extraction, also known as dimensionality reduction, is the transformation of high‐dimensional data into a meaningful representation of reduced dimensionality, which should have a dimensionality that corresponds to the intrinsic dimensionality of the data. Linear discriminant analysis (LDA) is commonly used as a dimensionality reduction technique in the data pre‐processing step for classification and machine learning applications. Feature selection, also called variable selection/attribute selection, is the process of selecting a subset of relevant features for use in model construction. The synthetic minority oversampling technique (SMOTE) algorithm produces artificial data based on the feature space similarities between minority data points.

  • A Brief Introduction to Machine Learning for Engineers

    There is a wealth of literature and books available to engineers starting to understand what machine learning is and how it can be used in their everyday work. This presents the problem of where the engineer should start. The answer is often “for a general, but slightly outdated introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning, this text is useful” and so on. This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment, encompassing recent developments and pointers to the literature for further study. A Brief Introduction to Machine Learning for Engineers is the entry point to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra.

  • Lifelong Machine Learning: Second Edition

    _Lifelong Machine Learning, Second Edition_ is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

  • Lifelong Machine Learning

    _Lifelong Machine Learning_ (or _Lifelong Learning_) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns _in isolation_: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this _isolated learning paradigm_ has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.



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