Conferences related to Machine learning algorithms

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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


2019 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR)

Topics in RF/microwave power amplifiers


2019 International Conference on Robotics and Automation (ICRA)

Flagship conference of the robotics and automation society, a premiere international venue for international robotics researchers


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.


ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

The 2019 IEEE International Conference on Communications (ICC) will be held from 20-24 May 2019 at Shanghai International Convention Center, China,conveniently located in the East Coast of China, the region home to many of the world’s largest ICT industries and research labs. Themed“Smart Communications”, this flagship conference of IEEE Communications Society will feature a comprehensive Technical Program including16 Symposia and a number of Tutorials and Workshops. IEEE ICC 2019 will also include an attractive Industry Forum & Exhibition Program featuringkeynote speakers, business and industry pan


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Periodicals related to Machine learning algorithms

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


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 ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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Most published Xplore authors for Machine learning algorithms

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Xplore Articles related to Machine learning algorithms

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Notice of Violation of IEEE Publication Principles<br>A Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining

2006 International Conference on Machine Learning and Cybernetics, 2006

Similarity measure between time series is a key issue in data mining of time series database. Euclidean distance measure is typically used init. However, the measure is an extremely brittle distance measure. Dynamic time warping (DTW) is proposed to deal with this case, but its expensive computation limits its application in massive datasets. In this paper, we present a new ...


II Many Ways to Learn

The Deep Learning Revolution, None

None


DATA ANALYTICS AND PREDICTIVE ANALYTICS IN THE ERA OF BIG DATA

Internet of Things and Data Analytics Handbook, None

This chapter outlines the key principles of machine learning and predictive analytics. It explains the new fundamentals of big data and the evolving technology. The chapter follows by the practical advice on how organizations can establish a new culture in order to truly transform their business in the new era. The wave of data frenzy did not happen overnight. Rather, ...


8 Managing the Organizational, Social, and Ethical Implications of AI

The AI Advantage: How to Put the Artificial Intelligence Revolution to Work, None

It is widely agreed that there are profound implications for organizations and societies from artificial intelligence. I've already discussed some of the employment issues that may arise from advances in AI. In addition to those, many observers have begun to comment on the various social and ethical issues that may come to the fore as AI becomes more intelligent and ...


Industry Track Abstracts

2016 IEEE International Conference on Healthcare Informatics (ICHI), 2016

Provides an abstract for each of the presentations and may include a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.


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Educational Resources on Machine learning algorithms

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IEEE.tv Videos

Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
Keynote: Symbiotic Autonomous Systems: The Fading Boundaries of the Cyberspace & Their Impact on Communities & Society - Derrick de Kerckhove
Spiking Network Algorithms for Scientific Computing - William Severa: 2016 International Conference on Rebooting Computing
The Path to Robust Machine Learning: IEEE TechEthics Keynote with Richard Mallah
Machine Learning of Motor Skills for Robotics
Linear Regression: Intro to Machine Learning Workshop - IEEE Region 4 Presentation
Data and Algorithmic Bias in the Web - Ricardo Baeza-Yates - WCCI 2016
Robotics History: Narratives and Networks Oral Histories: Dan Lee
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
Signal Processing and Machine Learning
Accelerating Machine Learning with Non-Volatile Memory: Exploring device and circuit tradeoffs - Pritish Narayanan: 2016 International Conference on Rebooting Computing
ICASSP 2011 Trends in Machine Learning for Signal Processing
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
Life Through the Eyes of a Machine
Bias in the Age of the Algorithm | IEEE TechEthics Virtual Panel
Dictionary Learning: Principles, Algorithms, Guarantees
Large Scale Data Mining Using Genetics-Based Machine Learning 1
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 2

IEEE-USA E-Books

  • Notice of Violation of IEEE Publication Principles<br>A Local Segmented Dynamic Time Warping Distance Measure Algorithm for Time Series Data Mining

    Similarity measure between time series is a key issue in data mining of time series database. Euclidean distance measure is typically used init. However, the measure is an extremely brittle distance measure. Dynamic time warping (DTW) is proposed to deal with this case, but its expensive computation limits its application in massive datasets. In this paper, we present a new distance measure algorithm, called local segmented dynamic time warping (LSDTW), which is based on viewing the local DTW measure at the segment level. The DTW measure between the two segments is the product of the square of the distance between their mean times the number of points of the longer segment. Experiments about cluster analysis on the basis of this algorithm were implemented on a synthetic and a real world dataset comparing with Euclidean and classical DTW measure. The experiment results show that the new algorithm gives better computational performance in comparison to classical DTW with no loss of accuracy

  • II Many Ways to Learn

    None

  • DATA ANALYTICS AND PREDICTIVE ANALYTICS IN THE ERA OF BIG DATA

    This chapter outlines the key principles of machine learning and predictive analytics. It explains the new fundamentals of big data and the evolving technology. The chapter follows by the practical advice on how organizations can establish a new culture in order to truly transform their business in the new era. The wave of data frenzy did not happen overnight. Rather, it is a crescendo of events happening since the early 1980s where the fields of business intelligence and predictive analytics were known as 'data mining', a preexisting discipline with another closely related term known as knowledge discovery in databases (KDD), which is the aim of performing data mining. Analytics has a spectrum of methodologies, techniques, and approaches from descriptive, diagnostic, predictive and prescriptive analytics. Most data mining projects today follow the cross industry standard process for data mining (CRISP‐DM) which was conceived in 1996.

  • 8 Managing the Organizational, Social, and Ethical Implications of AI

    It is widely agreed that there are profound implications for organizations and societies from artificial intelligence. I've already discussed some of the employment issues that may arise from advances in AI. In addition to those, many observers have begun to comment on the various social and ethical issues that may come to the fore as AI becomes more intelligent and more widely adopted.

  • Industry Track Abstracts

    Provides an abstract for each of the presentations and may include a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.

  • An Accelerated Linearly Convergent Stochastic L-BFGS Algorithm

    The limited memory version of the Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm is the most popular quasi-Newton algorithm in machine learning and optimization. Recently, it was shown that the stochastic L-BFGS (sL-BFGS) algorithm with the variance-reduced stochastic gradient converges linearly. In this paper, we propose a new sL-BFGS algorithm by importing a proper momentum. We prove an accelerated linear convergence rate under mild conditions. The experimental results on different data sets also verify this acceleration advantage.

  • Time series classification using Gaussian mixture models of reconstructed phase spaces

    A new signal classification approach is presented that is based upon modeling the dynamics of a system as they are captured in a reconstructed phase space. The modeling is done using full covariance Gaussian mixture models of time domain signatures, in contrast with current and previous work in signal classification that is typically focused on either linear systems analysis using frequency content or simple nonlinear machine learning models such as artificial neural networks. The proposed approach has strong theoretical foundations based on dynamical systems and topological theorems, resulting in a signal reconstruction, which is asymptotically guaranteed to be a complete representation of the underlying system, given properly chosen parameters. The algorithm automatically calculates these parameters to form appropriate reconstructed phase spaces, requiring only the number of mixtures, the signals, and their class labels as input. Three separate data sets are used for validation, including motor current simulations, electrocardiogram recordings, and speech waveforms. The results show that the proposed method is robust across these diverse domains, significantly outperforming the time delay neural network used as a baseline.

  • Visual Perceptual Learning

    Perceptual learning should be considered as an active process that embeds particular abstraction, reformulation and approximation within the Abstraction framework. The active process refers to the fact that the search for a correct data representation is perfonned through several steps. A key point is that perceptual learning focuses on low-level abstraction mechanism instead of trying to rely on more complex algorithm. In fact, from the machine learing viewpoint, Perceptual learning can be seen as a particular abstraction that may help to simplify complex problem thanks to a computable representation. Indeed, the baseline of Abstraction, i.e. choosing the relevant data to ease the learning task, is that many problems in machine learning cannot be solve because of the complexity of the representation and is not related to the learning algorithm, which is referred to as the phase transition problem. Within the Abstraction framework, we use the term perceptual learning to refer to specific learning task that rely on iterative representation changes and that deals with real-world data which human can perceive. In this talk we focus on sparse coding theory and granular computing model for visual perceptual learning. We propose an attention-guided sparse coding model. This model includes two modules: nonuniform sampling module simulating the process of retina and data-driven attention module based on the response saliency. Based on tolerance relation we construct a more uniform granulation model, which is established on both consecutive space and discrete attribute space.

  • A partially supervised learning algorithm for linearly separable systems

    An important aspect of human learning is the ability to select effective samples to learn and utilize the experience to infer the outcomes of new events. This type of learning is characterized as partially supervised learning. A learning algorithm of this type is suggested for linearly separable systems. The algorithm selects a subset S from a finite set X of linearly separable vectors to construct a linear classifier that can correctly classify all the vectors in X. The sample set S is chosen without any prior knowledge of how the vectors in X-S are classified. The computational complexity of the algorithm is analyzed, and the lower bound on the size of the sample set is established.<<ETX>>

  • Learning Mechanism of Automated Negotiation in E-commerce

    Aiming at the shortcoming of current automated negotiation systems, this paper applied machine learning to bilateral automated negotiation. It mainly researched learning mechanism of automated negotiation in e-commerce. It improved traditional Q-learning and designed dynamic Q-learning algorithm. This algorithm estimated Q value according to environment state and the action of both agents, furthermore, recency-based exploration bonus were embedded. The paper applied Bayesian learning to negotiation strategy of automated negotiation, and designed the belief strategy based on Bayesian. Finally, this paper did experiments on learning mechanism and dynamic Q-learning algorithm. The results show that learning mechanism can improve efficiency of automated negotiation and dynamic Q-learning algorithm is efficient



Standards related to Machine learning algorithms

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No standards are currently tagged "Machine learning algorithms"