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international conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Sheng Zhang Peixin Ye School of Mathematical Science and LPMC Nankai University Tianjin China

Nonlinear approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper we study the efficiency of greedy algorithm for nonlinear approximation. We estimate the... 详细信息

关键词： Approximation methods Greedy algorithms Tensile stress Approximation algorithms Hilbert space signal processing algorithms Presses

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Outlier detection for machine olfaction based on odor-type signatures

Outlier detection for machine olfaction based on odor-type s...

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international conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Ekachai Phaisangittisagul Electrical Engineering Department Faculty of Engineering Kasetsart University Bangkok Thailand

A method for detecting or identifying odor outlier sample plays an essential role in implementing machine olfaction, called electronic nose (e-nose). the benefit of removing outlier not only eases the classification design process but also helps to improve the classification performance of the e-nose. In this study, odor-type signatures derived from the sensor array's response waveforms are employed to detect the odor sample with high dimensionality that deviates in some degree from other odor samples. Four odor samples used for investigation consist of bacteria, coffee, soda, and rice with varying data quality. the experimental performance of the purposed method shows promising results to detect odor outlier.

关键词： Sensor arrays Array signal processing signal sampling Electronic noses Process design Covariance matrix machine learning Algorithm design and analysis Classification algorithms Degradation

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A survey of machine learning in wireless sensor netoworks - From networking and application perspectives

A survey of machine learning in wireless sensor netoworks - ...

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2007 6th international conference on Information, Communications and signal processing, ICICS

作者： Di, Ma Er, Meng Joo School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)1424409837

Wireless Sensor Networks (WSNs) are used to collect data from and make inferences about the environments or objects that they are sensing. these sensors are usually characterized by limited communication capabilities due to energy and bandwidth constraints. As a result, WSNs have inspired resurgence in research on machine learning methodologies with the objective of overcoming the physical constraints of sensors. In this paper, machine learning methods that have been applied in WSNs to solve some networking and application problems are surveyed. Fundamental limits of learning algorithms will be addressed and future machine learning research direction are highlighted. ©2007 IEEE.

关键词： Wireless sensor networks

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machine learning for tongue diagnosis

Machine learning for tongue diagnosis

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2007 6th international conference on Information, Communications and signal processing, ICICS

作者： Siu, Cheung Hui He, Yulan thach, Doan thi Cam School of Computer Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)1424409837

Tongue diagnosis is an important inspection method in Traditional Chinese Medicine (TCM). In this paper, we investigate machine learning techniques for tongue diagnosis. To do this, we first identify tongue properties and classes. In tongue property identification, we identify 21 properties from tongue substance and coating, whereas in tongue classification, we derive 24 tongue classes. machine learning techniques are then applied to a tongue dataset. In performance analysis, we use the Weka machine learning environment for conducting the experiment. Five different machine learning algorithms including ID3, J48, Naïve Bayes, BayesNet and SMO are used and applied to a tongue dataset of 457 instances. the performance results have shown that the Support Vector machine algorithm SMO has the best performance for tongue diagnosis based on accuracy and Area Under the ROC Curve (AUC). ©2007 IEEE.

关键词： learning algorithms

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Application of Rough Sets in image median filter

Application of Rough Sets in image median filter

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6th international conference on machine learning and Cybernetics

作者： Jiang, Li-Hui Zhang, Fan Jin, Zhen-Ni Liu, Rui-Hua Tianjin Key Lab Adv Signal Proc Civil Aviat Univ Tianjin 300300 Peoples R China

ISBN: (纸本)9781424409723

An improved median filter algorithm based on Rough Sets is presented in the paper. the algorithm first divides the image into several parts and uses Rough Sets theory to classify the pixel in each part then noise pixels can be separated and be removed by median filtering. Compared with standard median filter, the results of computer simulation experiments show that this new filter has better filtering performance and can preserve detail information of image better.

关键词： Rough Sets median filtering image processing

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Pixel-level image fusion based on fuzzy theory

Pixel-level image fusion based on fuzzy theory

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6th international conference on machine learning and Cybernetics

作者： Liu, Gang Lu, Xue-Qin Shanghai Univ Elect Power Sch Power & Automat Engn Shanghai 200093 Peoples R China

ISBN: (纸本)9781424409723

A new region based image fusion scheme is proposed. It is based on multiscale analysis. the low frequency band of the image multiscale representation is segmented into three kinds of regions by K-mean algorithm, which is used to determine the fusion rule and to achieve the multiscale representation of fusion result. the final image fusion result can be obtained by performing the inverse multiscale transform. the experiment demonstrates that the proposed image fusion method can illustrate better performance than exiting image fusion method.

关键词： information fusion image fusion signal processing fuzzy theory

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Morphological shared-weight probabilistic neural networks for pattern classification of SAR images

Morphological shared-weight probabilistic neural networks fo...

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6th international conference on machine learning and Cybernetics

作者： Guo, Yan-Ying Jiang, Li-Hui Civil Aviat Univ China Intelligence Signal & Image Proc Key Lab Tianjin Tianjin 300300 Peoples R China

ISBN: (纸本)9781424409723

In this paper we describe the application of morphological shared-weight probabilistic neural networks to the problems of pattern classification in synthetic aperture radar (SAR) images. the feature extraction process is learned by interaction with the classification process. Feature extraction is performed using gray-scale hit- miss transforms that are independent of gray-level shifts. the classification process is performed by probabilistic neural networks(PNN). Classification experiments were carried out with SAR images of military objects. And classification results show MSPNN architecture to optimize object recognition versus processing time and veracity.

关键词： pattern classification morphological shared-weight probabilistic neural networks SAR images

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A survey of machine learning in Wireless Sensor netoworks From networking and application perspectives

A survey of machine learning in Wireless Sensor netoworks Fr...

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international conference on Information, Communications and signal processing

作者： Ma Di Er Meng Joo School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781424409822

Wireless sensor networks (WSNs) are used to collect data from and make inferences about the environments or objects that they are sensing. these sensors are usually characterized by limited communication capabilities due to energy and bandwidth constraints. As a result, WSNs have inspired resurgence in research on machine learning methodologies with the objective of overcoming the physical constraints of sensors. In this paper, machine learning methods that have been applied in WSNs to solve some networking and application problems are surveyed. Fundamental limits of learning algorithms will be addressed and future machine learning research direction are highlighted.

关键词： Wireless sensor networks machine learning machine learning algorithms learning systems Sensor phenomena and characterization Information processing Bandwidth Routing Patient monitoring Construction industry

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the Berlin Brain-Computer Interface (BBCI) -: towards a new communication channel for online control in gaming applications

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MULTIMEDIA TOOLS AND APPLICATIONS 2007年第1期33卷 73-90页

作者： Krepki, Roman Blankertz, Benjamin Curio, Gabriel Mueller, Klaus-Robert Fraunhofer Inst Comp Architecture & Software Tech Res Grp Intelligent Data Anal D-61462 Koenigstein I Ts Germany Free Univ Berlin Klinikum Benjamin Franklin Dept Neurol Neurophys Grp D-12203 Berlin Germany Univ Potsdam Dept Comp Sci D-14482 Potsdam Germany

the investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future multimedia research and development. Brain-Computer Interfaces (BCI) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as Amyotrophic Lateral Sclerosis (ALS) or spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e. g., a dexterity game, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in electroencephalographic (EEG) signals which are recorded non-invasively from user's scalp. this contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents setups where the user is provided with intuitive control strategies in plausible gaming applications that use biofeedback. Yet at its beginning, BBCI thus adds a new dimension in multimedia research by offering the user an additional and independent communication channel based on brain activity only. First successful experiments already yielded inspiring proofs-of-concept. A diversity of multimedia application models, say computer games, and their specific intuitive control strategies, as well as various Virtual Reality (VR) scenarios are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates.

关键词： brain-computer interface electroencephalography digital signal processing machine learning biofeedback human-computer interaction brain-gaming

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machine learning for tongue diagnosis 6th

Machine learning for tongue diagnosis

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international conference on Information, Communications and signal processing

作者： Siu Cheung Hui Yulan He Doan thi Cam thach School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781424409822;1424409829

Tongue diagnosis is an important inspection method in Traditional Chinese Medicine (TCM). In this paper, we investigate machine learning techniques for tongue diagnosis. To do this, we first identify tongue properties and classes. In tongue property identification, we identify 21 properties from tongue substance and coating, whereas in tongue classification, we derive 24 tongue classes. machine learning techniques are then applied to a tongue dataset. In performance analysis, we use the Weka machine learning environment for conducting the experiment. Five different machine learning algorithms including ID3, J48, Naive Bayes, BayesNet and SMO are used and applied to a tongue dataset of 457 instances. the performance results have shown that the Support Vector machine algorithm SMO has the best performance for tongue diagnosis based on accuracy and Area Under the ROC Curve (AUC).

关键词： machine learning Tongue Inspection Coatings Medical diagnostic imaging Humans Diseases machine learning algorithms Support vector machines Fluids and secretions

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