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Incremental kNN classifier exploiting correct-error teacher for activity recognition

Incremental kNN classifier exploiting correct-error teacher ...

引用

9th international conference on Machine Learning and Applications, ICMLA 2010

作者： Förster, Kilian Monteleone, Samuel Calatroni, Alberto Roggen, Daniel Tröster, Gerhard Wearable Computing Laboratory ETH Zürich Switzerland

ISBN: (纸本)9780769543000

Non-stationary data distributions are a challenge in activity recognition from body worn motion sensors. Classifier models have to be adapted online to maintain a high recognition performance. Typical approaches for online learning are either unsupervised and potentially unstable, or require ground truth information which may be expensive to obtain. As an alternative we propose a teacher signal that can be provided by the user in a minimally obtrusive way. It indicates if the predicted activity for a feature vector is correct or wrong. To exploit this information we propose a novel incremental online learning strategy to adapt a k-nearest-neighbor classifier from instances that are indicated to be correctly or wrongly classified. We characterize our approach on an artificial dataset with abrupt distribution change that simulates a new user of an activity recognition system. The adapted classifier reaches the same accuracy as a classifier trained specifically for the new data distribution. The learning based on the provided correct - error signal also results in a faster learning speed compared to online learning from ground truth. We validate our approach on a real world gesture recognition dataset. The adapted classifiers achieve an accuracy of 78.6% compared to the subject independent baseline of 68.3%. © 2010 IEEE.

关键词： pattern recognition

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Violence detection in movies with auditory and visual cues

Violence detection in movies with auditory and visual cues

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international conference on Computational Intelligence and Security

作者： Lin, Jian Sun, Yi Wang, Weiqiang School of Information Science and Engineering Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Intell. Info. Process. Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9780769542973

In this paper, we present a novel method to detect violent scenes in movies. The detection process involves two views - audio and video views. For the audio view, a supervised method based on HCRFs is exploited to improve the classification performance. For the video view, we detect violent shots by locating the violent areas. Finally, the auditory and visual classifiers are combined in a co-training way. The experimental results on several movies with violent contents preliminarily show the effectiveness of our method. © 2010 IEEE.

关键词： pattern recognition

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Collecting complex activity datasets in highly rich networked sensor environments

Collecting complex activity datasets in highly rich networke...

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7th international conference on Networked Sensing Systems, INSS 2010

作者： Roggen, Daniel Calatroni, Alberto Rossi, Mirco Holleczek, Thomas Förster, Kilian Tröster, Gerhard Lukowicz, Paul Bannach, David Pirkl, Gerald Ferscha, Alois Doppler, Jakob Holzmann, Clemens Kurz, Marc Holl, Gerald Chavarriaga, Ricardo Sagha, Hesam Bayati, Hamidreza Creatura, Marco Del R. Millàn, Jose Wearable Computing Laboratory ETH Zurich Switzerland Institute for Pervasive Computing Johannes Kepler University Linz Austria Institute for Pervasive Computing Johannes Kepler University Linz Austria Defitech Foundation Chair in Non-Invasive Brain-Machine Interface Ecole Polytechnique Fédérale Lausarme Switzerland Department of Informatics Systems and Telematics University of Genova Italy

ISBN: (纸本)9781424479108

We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors;there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public. © 2010 IEEE.

关键词： pattern recognition

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Statistical Learning Theory and Support Vector Machines

Statistical Learning Theory and Support Vector Machines

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international conference on Computer Research and Development (ICCRD)

作者： Dewi Nasien Siti S. Yuhaniz Habibollah Haron Soft Computing Research Group Universiti Teknologi Malaysia Johor Bahru Malaysia

ISBN: (纸本)9781424469925;9780769540436

It has been more than 30 years that statistical learning theory (SLT) has been introduced in the field of machine learning. Its objective is to provide a framework for studying the problem of inference that is of gaining knowledge, making predictions, making decisions or constructing models from a set of data. Support Vector Machine, a method based on SLT, then emerged and becoming a widely accepted method for solving real-world problems. This paper overviews the pattern recognition techniques and describes the state of art in SVM in the field of pattern recognition.

关键词： Statistical learning Support vector machines pattern recognition Artificial neural networks Signal processing pattern matching Biological neural networks Neurons Machine learning Support vector machine classification

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An intelligent system for diagnosing sleep stages using wavelet coefficients

An intelligent system for diagnosing sleep stages using wave...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 international Joint conference on Neural Networks, IJCNN 2010

作者： Vatankhah, Maryam Akbarzadeh-T., Mohammad-R. Moghimi, Ali Islamic Azad University Mashhad Branch Iran Center for Applied Research on Intelligent Systems and Soft Computing Departments of Electrical Engineering and Computer Engineering Ferdowsi University of Mashhad Iran Dept. of Biology Faculty of Sciences Ferdowsi University of Mashhad [FUM Iran

ISBN: (纸本)9781424469178

Human sleep is divided into two segments, Rapid Eye Movement (REM) sleep and Non-REM (NREM) sleep. NREM sleep is further divided into 4 stages. Sleep staging attempts to identify these stages based on the signals collected in PSG. Significant information can be derived from the EEG signals collected during PSG. Wavelet coefficients are extracted from EEG signals. In order to reduce the amount of data set, the statistical features are calculated from wavelet coefficients. For performing decision making, six ANFIS classifiers and SVM classifier are used to differentiate between REM and Non-REM sleep stages. That is to say, pattern varies under the different sleep stages. Therefore, healthy humans with a regular night's sleep will follow these sleep stages in a particular pattern. © 2010 IEEE.

关键词： Intelligent systems

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Automatic Extraction of Linguistic Models for Image Description

Automatic Extraction of Linguistic Models for Image Descript...

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2010 IEEE World Congress on Computational Intelligence

作者： Baturone, Iluminada Gersnoviez, Andres Univ Seville C Amer Vespucio S-N Seville 41092 Spain Univ Cordoba Cordoba Spain

ISBN: (纸本)9781424469208

This paper describes a methodology to extract fuzzy models that describe linguistically the low-level features of an image (such as color, texture, etc.). The methodology combines grid-based algorithms with clustering and tabular simplification methods to compress image information into a small number of fuzzy rules with high linguistic meaning. All the steps of the methodology are carried out with the help offered by the tools of Xfuzzy 3 environment, so we can define, simplify, tune and verify the fuzzy models automatically. Several examples are included to illustrate the advantages of the methodology.

关键词： clustering methods computational linguistics feature extraction feature extraction fuzzy logic fuzzy models fuzzy rules grid computing grid-based algorithms image coding image description image information compression image recognition linguistic models pattern clustering tabular simplification methods

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Proceedings - 3rd international conference on Emerging Trends in Engineering and Technology, ICETET 2010

Proceedings - 3rd International Conference on Emerging Trend...

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3rd international conference on Emerging Trends in Engineering and Technology, ICETET 2010

ISBN: (纸本)9780769542461

The proceedings contain 163 papers. The topics discussed include: human computer interaction;a secure agent based intelligent tutoring system using FRS;amalgamating contextual information into recommender system;from fuzzification to neutrosophication:a better interface between logic and human reasoning;nonfuzzy classification using rules annotated with weight of evidence from statistical data;AutoBot: a low cost platform for swarm research applications;a design approach to traffic flow forecasting with soft computing tools;wireless transmission impact on the lifetime of routing path in VANET;Chinese numeral recognition using Gabor and SVM;novel approach to segmentation of handwritten Devnagari word;face detection using fuzzy logic and skin color segmentation in images;encrypting informative color image using color visual cryptography;and analysis and mitigation of balanced voltage sag with the help of energy storage system.

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FAULT DIAGNOSIS OF BALL BEARINGS USING soft computing

FAULT DIAGNOSIS OF BALL BEARINGS USING SOFT COMPUTING

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ASME international Design Engineering Technical conferences/Computers and Information in Engineering conference

作者： Bhavaraju, Kalyan M. Kankar, P. K. Sharma, Satish C. Harsha, S. P. Indian Inst Technol Dept Mech & Ind Engn Noise & Vibrat Control Lab Roorkee 247667 Uttarkhand India

ISBN: (纸本)9780791848982

This paper presents the condition monitoring and fault diagnosis of rolling element bearings using Support Vector Machines (SVM). The vibration response of healthy bearings and bearings with various component defects such as outer race, inner race, balls and their combination have been analyzed. From the obtained vibration spectrum, it is clearly seen that a discrete peak of excitation appeared for the specific defect of bearings. In this paper, various faults of the bearings has been simulated and classified. The process includes, data acquisition, feature extraction from time response and a knowledge based system to classify faults. Features defining feature vectors are formed using statistical techniques and are fed as input to the support vector machine (SVM) classifiers. Knowledge based system developed for classification can be used for automatic recognition of machinery faults based on feature vector.

关键词： Machinery Rolling bearings Condition monitoring Feature extraction Data acquisition Excitation Ball bearings Vibration Fault diagnosis Support vector machines Bearings Vibrational spectra

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Ubiquitous Intelligence and computing - 7th international conference, UIC 2010, Proceedings

Ubiquitous Intelligence and Computing - 7th International Co...

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7th international conference on Ubiquitous Intelligence and computing, UIC 2010

ISBN: (纸本)3642163548

The proceedings contain 48 papers. The topics discussed include: programming pervasive spaces;the operating system for the computer of the 21st century;extracting social and community intelligence from digital footprints: an emerging research area;smart itinerary recommendation based on user-generated GPS trajectories;inferring user search intention based on situation analysis of the physical world;ontology-enabled activity learning and model evolution in smart homes;support vector machines for inhabitant identification in smart houses;towards non-intrusive sleep pattern recognition in elder assistive environment;the making of a dataset for smart spaces;introduction to the business processes with ambient media - challenges for ubiquitous and pervasive systems;alerting accidents with ambiguity: a tangible tabletop application for safe and independent chemistry experiments;and dependency relation based detection of lexicalized user goals.

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Approximated maximum likelihood bearing estimation based on ant colony algorithm

Approximated maximum likelihood bearing estimation based on ...

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2010 international conference on Web Information Systems and Mining, WISM 2010

作者： Zhai, Hongcun Hou, Yunshan Jin, Yong College of Mathematical Science Luoyang Normal University Luoyang 471022 China College of Marine Northwestern Polytechnical University Xi'an 710072 China Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China

ISBN: (纸本)9780769542249

It is well known that Approximated Maximum Likelihood(AML) estimator has the best performance for short time sampling wideband source bearing estimation. But for a long time, the heavy computational load of maximizing the multivariate, highly non-linear likelihood function prevented it from popular use. In this paper, we introduced Ant Colony Algorithm (ACA) to work with AML for computing the exact solutions to the likelihood function with a guarantee of global convergence. The resulted estimator is called Approximated Maximum Likelihood bearing estimator based on Ant Colony Algorithm (ACA-AML). Simulations show that ACA-AML not only reduces the computational complexity greatly but also maintains the excellent performance of the original AML estimator. © 2010 IEEE.

关键词： Maximum likelihood estimation

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