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共 585 条记录，以下是401-410 订阅

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Combining fisher linear discriminants for dissimilarity representations 1

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1st international workshop on Multiple Classifier Systems, MCS 2000

作者： Pȩkalska, Elzbieta Skurichina, Marina Duin, Robert P.W. Pattern Recognition Group Department of Applied Physics Faculty of Applied Sciences Lorentzweg 1 2628 CJ Delft Netherlands

ISBN: (数字)9783540450146

ISBN: (纸本)3540677046

Investigating a data set of the critical size makes a classification task difficult. studying dissimilarity data refers to such a problem, since the number of samples equals their dimensionality. In such a case, a simple classifier is expected to generalize better than the complex one. Earlier experiments [9,3] confirm that in fact linear decision rules perform reasonably well on dissimilarity representations. For the Pseudo-Fisher linear discriminant the situation considered is the most inconvenient since the generalization error approaches its maximum when the size of a learning set equals the dimensionality [10]. However, some improvement is still possible. Combined classifiers may handle this problem better when a more powerful decision rule is found. In this paper, the usefulness of bagging and boosting of the Fisher linear discriminant for dissimilarity data is discussed and a newmethod based on random subspaces is proposed. This technique yields only a single linear pattern recognizer in the end and still significantly improves the accuracy. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Classification (of information)

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Early Experiences with Crowdsourcing Airway Annotations in Chest CT 1st

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2nd international workshop on Deep learning in Medical Image Analysis (DLMIA) / 1st international workshop on Large-Scale Annotation of Biomedical data and Expert Label Synthesis (LABELS)

作者： Cheplygina, Veronika Perez-Rovira, Adria Kuo, Wieying Tiddens, Harm A. W. M. de Bruijne, Marleen Erasmus MC Biomed Imaging Grp Rotterdam Dept Med Informat Rotterdam Netherlands Erasmus MC Dept Radiol Rotterdam Netherlands Delft Univ Technol Pattern Recognit Lab Delft Netherlands Sophia Childrens Univ Hosp Erasmus Med Ctr Dept Pediat Pulmonol & Allergol Rotterdam Netherlands Erasmus MC Dept Radiol Rotterdam Netherlands Univ Copenhagen Dept Comp Sci Image Sect Copenhagen Denmark

ISBN: (纸本)9783319469768;9783319469751

Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. machine learning algorithms offer an alternative, but need large sets of annotated data to perform well. We investigate whether crowdsourcing can be used to gather airway annotations which can serve directly for measuring the airways, or as training data for the algorithms. We generate image slices at known locations of airways and request untrained crowd workers to outline the airway lumen and airway wall. Our results show that the workers are able to interpret the images, but that the instructions are too complex, leading to many unusable annotations. After excluding unusable annotations, quantitative results show medium to high correlations with expert measurements of the airways. Based on this positive experience, we describe a number of further research directions and provide insight into the challenges of crowdsourcing in medical images from the perspective of first-time users.

关键词： learning systems

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Online Audience Measurement System Based on machine learning Techniques

引用

1st international workshop on Video Analytics for Audience Measurement (VAAM)

作者： Khryashchev, Vladimir Priorov, Andrey Ganin, Alexander PG Demidov Yaroslavl State Univ Yaroslavl Russia

ISBN: (纸本)9783319128115;9783319128108

An application for video data analysis based on computer vision methods is presented. The proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of tracking. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 92 % accuracy of viewer's gender recognition is achieved. All the stages are united into a single system of audience analysis. The system allows to extract all the possible information about depicted people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters.

关键词： Online audience measurement machine learning Gender classification Age estimation Local binary patterns Support vector machines

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machine learning and dataming Algorithms for Predicting Accidental Small Forest Fires 5

Machine Learning and Dataming Algorithms for Predicting Acci...

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5th international Conference on Sensor Technologies and Applications (SENSORCOMM) / 1st international workshop on Sensor Networks for Supply Chain Management (WSNSCM)

作者： Iyer, Vasanth Iyengar, S. Sitharama Paramesh, N. Murthy, Garmiela Rama Srinivas, Mandalika B. Int Inst Informat Technol Hyderabad 500032 India Louisiana State Univ Baton Rouge LA 70803 USA Univ New South Wales Sydney Australia Brila Inst Technol & Sci Hyderabad 500078 India

ISBN: (纸本)9781612081441

Extracting useful temporal and spatial patterns from sensor data has been seen before, the technical basis of machine learning with data mining is studied with the evidence collected uniformly over many years and which allow using users' perspective in collected evidence. This model helps in probabilistically forecasting fires and help forest department in planing day to day schedules. Using a model to predict future events reliably one needs to collect samples from sensors and select a feature, which does have any particular bias. Due to practicable problems most of the collected data have 80% of attributes missing and the remaining has numeric values, which are hard to discretization. To adapt to such limitations, we use nominal data type, which allows better understanding of the temporal and spatial features, which are learnt. We encounter several practicable limitations as forest fires events are very rare and manual classification is extremely costly. Another is the unbalanced nature of the problem of the many forest fire events many are of the burnt area is very small and gives skewed distribution. Most of the examples naturally group into batches, which are collected from evidence satellite photography and collaborative reports from national parks departments. The second set of database was collected from the meteorological weather station about several weather observations, which are located very close to the reported fires. Finally, the compiling task is to serve as a filter and provide the user to vary the false alarm rate. We show by regression analysis of the compiled dataset that the forest fire classifier has a minimum false alarm rate when including temporal features. The machine learning algorithms successfully classifies accidental small fires with 85% reliably and large fires by a much lower accuracy of 30%.

关键词： machine learning datamining Naive Bayes Forest fires Fire Weather Index (FWI) Temporal patterns WEKA machine learning framework

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A fast SVM training algorithm 1st

A fast SVM training algorithm

引用

1st international workshop on pattern recognition with Support Vector machines, SVM 2002

作者： Dong, Jian-Xiong Krzyżak, Adam Suen, Ching Y. Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada Department of Computer Science Concordia University 1455 de Maisonneuve Blvd. W MontrealQCH3G 1M8 Canada

ISBN: (纸本)354044016X

A fast support vector machine (SVM) training algorithm is proposed under the decomposition framework of SVM’s algorithm by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Extensive experiments on MNIst handwritten digit database have been conducted to show that the proposed algorithm is much faster than Keerthi et al.’s improved SMO, about 9 times. Combined with principal component analysis, the total training for ten oneagainst-the-rest classifiers on MNIst took just 0. 77 hours. The promising scalability of the proposed scheme can make it possible to apply SVM to a wide variety of problems in engineering. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Support vector machines

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The distribution problem of unstructured data when solving data mining tasks on computer clusters 1

The distribution problem of unstructured data when solving d...

引用

1st international Conference of Information Systems and Design, ICID 2019

作者： Zein, Ali Najyevich Borisova, Svetlana Vyacheslavovna Moscow Power Engineering Institute National Research University Krasnokazarmennaya 14 Moscow Russia

In the modern world, the amount of information is growing exponentially, with every minute new data appears for further storage, processing and *** data sources generate huge amounts of data and most of them are presented in a format that does not correlate well with traditional structured database formats. The three main "commandments" of big data (3V) are Volume (large amount of information), Variety (heterogeneity and lack of structure) and Velocity (data processing speed) [1]. Imagine that there is a large amount of published information that needs to be sorted into sections, which will greatly speed up and simplify its reading and processing [2]. By machine learning can be attributed various methods of cluster analysis, with which you can automate the classification by finding patterns or patterns that describe this data. Using the method of cluster analysis of "K-means", it is possible to achieve the distribution of the K-number of clusters at the greatest possible distance from each other, thereby allowing us to assess how much they differ. Ideally, the differences will be quite large, which will show the quality of execution, thereby significantly accelerating the process of analysis and information content. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： data mining

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Unsupervised Representation learning with Attention and Sequence to Sequence Autoencoders to Predict Sleepiness from Speech 1

Unsupervised Representation Learning with Attention and Sequ...

引用

1st international Multimodal Sentiment Analysis in Real-Life Media Challenge and workshop, MuSe 2020

作者： Amiriparian, Shahin Winokurow, Pawel Karas, Vincent Ottl, Sandra Gerczuk, Maurice Schuller, Björn Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Augsburg Germany

ISBN: (纸本)9781450381574

Motivated by the attention mechanism of the human visual system and recent developments in the field of machine translation, we introduce our attention-based and recurrent sequence to sequence autoencoders for fully unsupervised representation learning from audio files. In particular, we test the efficacy of our novel approach on the task of speech-based sleepiness recognition. We evaluate the learnt representations from both autoencoders, and conduct an early fusion to ascertain possible complementarity between them. In our frameworks, we first extract Mel-spectrograms from raw audio. Second, we train recurrent autoencoders on these spectrograms which are considered as time-dependent frequency vectors. Afterwards, we extract the activations of specific fully connected layers of the autoencoders which represent the learnt features of spectrograms for the corresponding audio instances. Finally, we train support vector regressors on these representations to obtain the predictions. On the development partition of the data, we achieve Spearman's correlation coefficients of .324, .283, and .320 with the targets on the Karolinska Sleepiness Scale by utilising attention and non-attention autoencoders, and the fusion of both autoencoders' representations, respectively. In the same order, we achieve .311, .359, and .367 Spearman's correlation coefficients on the test data, indicating the suitability of our proposed fusion strategy. © 2020 ACM.

关键词： learning systems

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Experimental evaluation on machine learning techniques for human activities recognition in digital education context 1

引用

1st international workshop on Social Computing in Digital Education, SOCIALEDU 2015

作者： Leitão, Gabriel Colonna, Juan Ribeiro, Erick Barreto, Raimundo Araujo, Thierry-Yves Martins, Anny Koster, Andrew Koch, Fernando Manaus Brazil Samsung Research Institute CampinasSP France

ISBN: (数字)9783319396729

ISBN: (纸本)9783319396712

This paper describes an experimental evaluation of the main machine learning supervised techniques to be used for the human activities recognition in the context of technological education using data collected from smartphones sensors. The overall goal is to use the recognition of activities to identify students with attention deficit or hyperactivity problems, by recognizing three activities: walking, standing and sitting. Hence, this work focuses on developing activities recognition method of the students. The methodology consists in: collecting data where the user explicitly states what activity he/she is doing;applying various techniques to automatically recognize the activities;and measuring the degree of accuracy of each technique. The results shows that techniques such as Bayesian inference and SVM (Support Vector machine) have smaller accuracy than techniques based on decision tree and kNN (k-nearest neighbors). Furthermore, the techniques based on decision trees have a constant computational cost, while the kNN depends on the number of samples. © Springer international Publishing Switzerland 2016.

关键词： students

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Optimal Feature Subset Selection for pattern Classification and recognition Using Spectral Entropy as a Complexity Measure from Alcohol and Sleep EEG data 1

Optimal Feature Subset Selection for Pattern Classification ...

引用

1st IEEE international Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Singh, Takveer Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350345957

The quantity of unprocessed data in multi-channel EEG analysis is enormous. Most datasets have some level of noise or redundancy, which could lower the accuracy of any relevant features that are uncovered. This result in a rise in computational complexity and a corresponding rise in the importance of concerns related to memory consumption. Consequently, feature selection has become the matter of significant research in the arenas of machine learning and statistics since it is crucial for efficient analysis of data while needing less memory and time. Based on the findings of this study, selecting features with high class separability from multichannel EEG recordings is an efficient method for performing supervised learning tasks. With this study, we intend to test the following theory: The best feature subset will have features that are substantially linked with the class labels but not connected with each other. Using the same alcoholic EEG database as these other investigations, we created and compared three feature selection algorithms. The purpose here is to use class knowledge in a feature selection technique to enhance the accuracy with which classes may be detected. © 2023 IEEE.

关键词： Feature Selection

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Efficient Large Scale Medical Image dataset Preparation for machine learning Applications 1

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1st MICCAI international workshop on data Engineering in Medical Imaging (DEMI)

作者： Denner, stefan Scherer, Jonas Kades, Klaus Bounias, Dimitrios Schader, Philipp Kausch, Lisa Bujotzek, Markus Bucher, Andreas Michael Penzkofer, Tobias Maier-Hein, Klaus German Canc Res Ctr Div Med Image Comp Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Natl Ctr Tumor Dis NCT Heidelberg Germany Goethe Univ Frankfurt Univ Hosp Inst Diagnost & Intervent Radiol Frankfurt Germany Charite Univ Med Berlin Dept Radiol Berlin Germany

ISBN: (数字)9783031449925

ISBN: (纸本)9783031449918;9783031449925

In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be facilitated in machine learning algorithms. This paper introduces an innovative data curation tool, developed as part of the Kaapana (https://***/kaapana/ kaapana) open-source toolkit, aimed at streamlining the organization, management, and processing of large-scale medical imaging datasets. The tool is specifically tailored to meet the needs of radiologists and machine learning researchers. It incorporates advanced search, auto-annotation and efficient tagging functionalities for improved data curation. Additionally, the tool facilitates quality control and review, enabling researchers to validate image and segmentation quality in large datasets. It also plays a critical role in uncovering potential biases in datasets by aggregating and visualizing metadata, which is essential for developing robust machine learning models. Furthermore, Kaapana is integrated within the Radiological Cooperative Network (RACOON), a pioneering initiative aimed at creating a comprehensive national infrastructure for the aggregation, transmission, and consolidation of radiological data across all university clinics throughout Germany. A supplementary video showcasing the tool's functionalities can be accessed at https://***/MICCAI- DEMI2023.

关键词： Medical Imaging data Curation machine learning Kaapana dataset Preperation Quality Control Bias Detection

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