IEEE – Machine Learning


Conferences related to Machine Learning

Back to Top2014 17th International Conference on Information Fusion – (FUSION 2014)

The conference is the forum to discuss advances, new results and applications of information fusion. Conference will include contributions in the areas of fusion methodologies, theory and representation, algorithms, modeling and performance analysis.
2014 IEEE Aerospace Conference

The international IEEE Aerospace Conference is organized to promote interdisciplinary understanding of aerospace systems, their underlying science, and technology
2014 IEEE Conference on Computational Intelligence and Games (CIG) 

Games can be used as a challenging scenery for benchmarking methods from computational intelligence since they provide dynamic and competitive elements that are germane to real-world problems. This conference brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in this field.
2014 IEEE International Conference on Systems, Man and Cybernetics – SMC

SMC2014 targets advances in Systems Science and Engineering, Human-Machine Systems, and Cybernetics involving state-of-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies, and emerging applications.
2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

AVSS focuses on video and signal based surveillance. Topics include: 1) Sensors and data fusion, 2) Processing, detection & recognition, 3) Analytics, behavior & biometrics, 4) Data management and human-computer interfaces, 5) Applications and 6) Privacy Issues
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Xplore Articles related to Machine Learning

Back to TopCooperative Machine Learning with Information Fusion for Dynamic Decision Making in Diagnostic Applications 

Vidhate, D.; Kulkarni, P. Advances in Mobile Network, Communication and its Applications (MNCAPPS), 2012 International Conference on, 2012

In many applications, use of all relevant data to extract more information from multiple sources of information and achieve higher accuracy in prediction is desirable. Cooperative learning is observed in human and some animal societies. Sound knowledge and information acquisition, cooperation in learning amongst multi-agent systems may result in a higher effectiveness compared to individual learning. Cooperative learning in multi …
Automatic classification of learning objects through dimensionality reduction and feature subset selections in an e-learning system 

Chellatamilan, T.; Suresh, R.M. Technology Enhanced Education (ICTEE), 2012 IEEE International Conference on, 2012

This Research paper focus on the design and development of intelligent, personalized mobile agent for learning object classification and retrieval. We have used JADE (Java Agent Development Environment) platform to launch, migrate, classify and retrieve the learning content based on the customized query by a peer learners in an virtual e-learning environment like MOODLE. In turn the agent collects the …
A Study and Application on Machine Learning of Artificial Intellligence 

Ming Xue; Changjun Zhu Artificial Intelligence, 2009. JCAI ’09. International Joint Conference on, 2009

This thesis elaborated the concept, significance and main strategy of machine learning as well as the basic structure of machine learning system. By combining several basic ideas of main strategies, great effort are laid on introducing several machine learning methods, such as Rote learning, Explanation-based learning, Learning from instruction, Learning by deduction, Learning by analogy and Inductive learning, etc. Meanwhile, …
Machine Learning Paradigms for Speech Recognition: An Overview 

Li Deng; Xiao Li Audio, Speech, and Language Processing, IEEE Transactions on, 2013

Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems …
Effective Integration of Imitation Learning and Reinforcement Learning by Generating Internal Reward 

Hamahata, K.; Taniguchi, T.; Sakakibara, K.; Nishikawa, I.; Tabuchi, K.; Sawaragi, T. Intelligent Systems Design and Applications, 2008. ISDA ’08. Eighth International Conference on, 2008

This paper describes an integrative machine learning architecture of imitation learning and reinforcement learning. The learning architecture aims to help integration of the two learning process by generating internal rewards. After observing superiors, human learners usually start practicing through trial and error. Humans usually learn tasks through both imitation learning and reinforcement learning. Imitation learning and reinforcement learning should be …
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Jobs related to Machine Learning

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Faculty position in Electronic and Computer Engineering  The Hong Kong University of Science and Technology
FACULTY POSITION – DATA SCIENCE METHODOLOGY  NYU Shanghai
Assistant Teaching Professor  Carnegie Mellon University
Research Faculty (Intelligent Control Department Head)  Pennsylvania State University
Senior Researcher Computer Science  Shell Oil Company
Director and UTC Endowed Chair Professor  University of Connecticut School of Engineering

Educational Resources on Machine Learning

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eLearning

Information Theoretic Learning 

Principe, Jose Information Theoretic Learning, 2006

This course examines Information Theory and our efforts to develop an information-theoretic criterion which can be utilized in adaptive filtering and neurocomputing. The main aim of our research is to develop new signal processing techniques by going beyond the basic assumptions of Linearity, Gaussianity and Stationarity. By capturing higher order statistics of data using Information Theory, we solve a variety …
Mathematical Foundations 

Henderson, Peter Mathematical Foundations, 2011

This tutorial is part of a series of eLearning courses designed to help you prepare for the examination to become a Certified Software Development Professional (CSDP) or to learn more about specific software engineering topics. Courses in this series address one or more of the fifteen Knowledge Areas that comprise the Software Engineering Body of Knowledge – or SWEBOK, upon …

IEEE.tv Videos

ICASSP 2011 Trends in Machine Learning for Signal Processing 
EMBC 2011-Panel Discussion-Frontiers and Future Trends in Brain-Machine Interface 
IEEE 125th Anniversary Media Event: Brain-Machine Interface Technology 
Moving into the Future with Smart Medicine: Thomas Schmitz-Rode 
IEEE Life Sciences: Martin Kohn Interview 
Technology Time Machine – Dr. James Truchard Presentation 
Feeding the Machine: The World’s Most Sophisticated Artificial Stomach 
EMBC 2012 Theme Speaker: Dr. Krishna V. Shenoy 
Trends in Signal Processing Education 
AM37x Sitara EVM Demonstration 
Larson Collection interview with John V. Atanasoff 
EMBC ’09 – Technology’s Role in Understanding and Treating Conditions of the Brain. 
ICASSP 2010 – Advances in Neural Engineering 
Social Robot Olivia 
Engineering Our Future – Q and A with Panel 
The Design of Wearable Robots for Lower-Extremity Human Augmentation 
Engineering The Future – Jon Spaihts Opening Remarks 
Larson Collection interview with Mark Oliphant 

IEEE-USA E-Books

No IEEE-USA E-Books are currently tagged “Machine Learning”


Standards related to Machine Learning

Back to TopNo standards are currently tagged “Machine Learning”


Periodicals related to Machine Learning

Back to TopConsumer Electronics, IEEE Transactions on 

The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes
Evolutionary Computation, IEEE Transactions on 

Papers on application, design, and theory of evolutionary computation, with emphasis given to engineering systems and scientific applications. Evolutionary optimization, machine learning, intelligent systems design, image processing and machine vision, pattern recognition, evolutionary neurocomputing, evolutionary fuzzy systems, applications in biomedicine and biochemistry, robotics and control, mathematical modelling, civil, chemical, aeronautical, and industrial engineering applications.
Fuzzy Systems, IEEE Transactions on 

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and …
Knowledge and Data Engineering, IEEE Transactions on 

Artificial intelligence techniques, including speech, voice, graphics, images, and documents; knowledge and data engineering tools and techniques; parallel and distributed processing; real-time distributed processing; system architectures, integration, and modeling; database design, modeling, and management; query design, and implementation languages; distributed database control; statistical databases; algorithms for data and knowledge management; performance evaluation of algorithms and systems; data communications aspects; system …
Robotics & Automation Magazine, IEEE 

It will build upon the existing newsletter base by adding high quality technical articles in the areas of: applied research, state of the shelf solutions and technologies, and education. Articles will be targeted toward the practicing engineer. Creative solutions to real-world problems will be emphasized. Implementation details will be highlighted. Tutorials will provide the technical and historical knowledge required to …
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Most published Xplore authors for Machine Learning

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