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检索条件"任意字段=ICMLA 2005: 4th International Conference on Machine Learning and Applications"

共 213 条记录，以下是41-50 订阅

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Price Forecasting in the Spanish Day-Ahead Electricity Market Using Preconditioned Wind Power Information

Price Forecasting in the Spanish Day-Ahead Electricity Marke...

引用

12th international conference on machine learning and applications (icmla)

作者： Geidel, C. Zareipour, H. Tech Univ Berlin Dept Elect Engn & Comp Sci Berlin Germany Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

ISBN: (纸本)9780769551449

In this paper, short-term electricity price forecasting using residual demand under predefined wind power generation conditions is performed. Residual demand is defined as the total electricity demand subtracted by hard to predict renewable energy sources. Focus of this paper lies on wind power generation as the main renewable energy source. First, the long-term influence of wind power on the electricity market price is investigated. Second, the short-term dependency between electricity market price and wind power generation is examined by applying the similar day method to the Spanish day-ahead market as well as data association mining. third, a novel method of how to use wind power information is introduced.

关键词： Price forecasting residual demand wind power

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Shape recognition and retrieval using string of symbols

Shape recognition and retrieval using string of symbols

引用

5th international conference on machine learning and applications

作者： Daliri, Mohammad Reza Torre, Vincent SISSA Via Beirut 2-4 I-34014 Trieste Italy

ISBN: (纸本)9780769527352

In this paper we present two algorithms for shape recognition. Both algorithms map the contour of the shape to be recognized into a string of symbols. the first algorithm is based on supervised learning using string kernels as often used for text categorization and classification. the second algorithm is very weakly supervised and is based on the Procrustes analysis and on the Edit distance used for computing the similarity between strings of symbols. the second algorithm correctly, recognizes 98.29 % of shapes from. the MPEG-7 database, i.e. better than any previous algorithms. the second algorithm is able also to retrieve similar shapes from a database.

关键词： Codes (symbols)

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LE-CapsNet: A Light and Enhanced Capsule Network 20

LE-CapsNet: A Light and Enhanced Capsule Network

引用

20th IEEE international conference on machine learning and applications (icmla)

作者： Shiri, Pouya Baniasadi, Amirali Univ Victoria ECE Dept Victoria BC Canada

ISBN: (纸本)9781665443371

Capsule Network (CapsNet) classifier has several advantages over CNNs, including better detection of images containing overlapping categories and higher accuracy on transformed images. Despite the advantages, CapsNet is slow due to its different structure. In addition, CapsNet is resource-hungry, includes many parameters and lags in accuracy compared to CNNs. In this work, we propose LE-CapsNet as a light, enhanced and more accurate variant of CapsNet. Using 3.8M weights, LE-CapsNet obtains 76.73% accuracy on the CIFAR-10 dataset while performing inference 4x faster than CapsNet. In addition, our proposed network is more robust at detecting images with affine transformations compared to CapsNet. We achieve 94.37% accuracy on the AffNIST dataset (compared to CapsNet's 90.52%).

关键词： Capsule Network CapsNet Deep learning Image Classification Fast CapsNet

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A Hybrid Scheme for Fault Diagnosis with Partially Labeled Sets of Observations 16

A Hybrid Scheme for Fault Diagnosis with Partially Labeled S...

引用

16th IEEE international conference on machine learning and applications (icmla)

作者： Razavi-Far, Roozbeh Hallaji, Ehsan Saif, Mehrdad Rueda, Luis Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada Univ Windsor Sch Comp Sci 401 Sunset Ave Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781538614174

machine learning techniques are widely used for diagnosing faults to guarantee the safe and reliable operation of the systems. Among various techniques, semi-supervised learning can help in diagnosing faulty states and decision making in partially labeled data, where only a few number of labeled observations along with a large number of unlabeled observations are collected from the process. thus, it is crucial to conduct a critical study on the use of semi-supervised techniques for both dimensionality reduction and fault classification. In this work, three state-of-the-art semi-supervised dimensionality reduction techniques are used to produce informative features for semi-supervised fault classifiers. this study aims to achieve the best pair of the semi-supervised dimensionality reduction and classification techniques that can be integrated into the diagnostic scheme for decision making under partially labeled sets of observations.

关键词： Fault diagnosis semi-supervised learning bearing defects dimensionality reduction induction motors

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Gaussian Mixture Model Cluster Forest 14

Gaussian Mixture Model Cluster Forest

引用

IEEE 14th international conference on machine learning and applications icmla

作者： Janousek, Jan Gajdos, Petr Radecky, Michal Snasel, Vaclav VSB Tech Univ Ostrava FEECS Dept Comp Sci 17 Listopadu 15 Ostrava 70833 Czech Republic VSB Tech Univ Ostrava Ctr Excellence IT4Innovat Ostrava 70833 Czech Republic

ISBN: (纸本)9781509002870

Random Forest (RF) classification algorithm is widely used in the area of information retrieval and became a basis for some extended branches of classification and/or regression algorithms. Cluster Forest (CF) represents a particular branch, and brings usually better results than individual clustering algorithms. this article describes a new ensemble clustering algorithm based on CF that internally uses a probabilistic model called Gaussian Mixture Model (GMM). Finally, Expectation-maximization algorithm is used for estimation of GMM parameters. the proposed ensemble clustering algorithm will be compared with several different approaches and tested on eight datasets.

关键词： Cluster Forest clustering ensemble clustering Gaussian Mixture Expectation maximization

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Nonlinear Dimensionality Reduction by Unit Ball Embedding (UBE) and its Application to Image Clustering 15

Nonlinear Dimensionality Reduction by Unit Ball Embedding (U...

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15th IEEE international conference on machine learning and applications (icmla)

作者： Soleimani, Behrouz Haji Matwin, Stan Dalhousie Univ Fac Comp Sci Inst Big Data Analyt 6050 Univ Ave Halifax NS B3H 4R2 Canada

ISBN: (纸本)9781509061679

the paper presents an unsupervised nonlinear dimensionality reduction algorithm called Unit Ball Embedding (UBE). Many high-dimensional data, such as object or face images, lie on a union of low-dimensional subspaces which are often called manifolds. the proposed method is able to learn the structure of these manifolds by exploiting the local neighborhood arrangement around each point. It tries to preserve the local structure by minimizing a cost function that measures the discrepancy between similarities of points in the high-dimensional data and similarities of points in the low-dimensional embedding. the cost function is proposed in a way that it provides a hyper-spherical representation of points in the low-dimensional embedding. Visualizations of our method on different datasets show that it creates large gaps between the manifolds and maximizes the separability of them. As a result, it notably improves the quality of unsupervised machine learning tasks (e.g. clustering). UBE is successfully applied on image datasets such as faces, handwritten digits, and objects and the results of clustering on the low-dimensional embedding show significant improvement over existing dimensionality reduction methods.

关键词： Cost functions

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machine learning for Plant Disease Incidence and Severity Measurements from Leaf Images 15

Machine Learning for Plant Disease Incidence and Severity Me...

引用

15th IEEE international conference on machine learning and applications (icmla)

作者： Owomugisha, Godliver Mwebaze, Ernest Univ Groningen Johann Bernoulli Inst Math & Comp Sci POB 407 NL-9700 AK Groningen Netherlands Makerere Univ Sch Comp & Informat Technol POB 7062 Kampala Uganda Busitema Univ Fac Engn POB 236 Tororo Uganda

ISBN: (纸本)9781509061679

In many fields, superior gains have been obtained by leveraging the computational power of machine learning techniques to solve expert tasks. In this paper we present an application of machine learning to agriculture, solving a particular problem of diagnosis of crop disease based on plant images taken with a smartphone. Two pieces of information are important here;the disease incidence and disease severity. We present a classification system that trains a 5 class classification system to determine the state of disease of a plant. the 5 classes represent a health class and 4 disease classes. We further extend the classification system to classify different severity levels for any of the 4 diseases. Severity levels are assigned classes 1-5, 1 being a healthy plant, 5 being a severely diseased plant. We present ways of extracting different features from leaf images and show how different extraction methods result in different performance of the classifier. We finally present the smartphone-based system that uses the classification model learnt to do real-time prediction of the state of health of a farmers garden. this works by the farmer uploading an image of a plant in his garden and obtaining a disease score from a remote server.

关键词： Agriculture

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applications of Mobile machine learning for Detecting Bio-energy Crops Flowers 20

Applications of Mobile Machine Learning for Detecting Bio-en...

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20th IEEE international conference on machine learning and applications (icmla)

作者： Zeng, Wenjun Amen, Bakhtiar Univ Liverpool Sch Elect Engn Elect & Comp Sci Liverpool Merseyside England Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781665443371

Automated flower detection and control is important to crop production and precision agriculture. Some computer vision methods have been proposed for flower detection, but their performances are not satisfactory on platforms with limited computing ability such as mobile and embedded devices, and thus not suitable for field applications. Herein we demonstrate two de novo approaches that can precisely detect the flowers of two bioenergy crops (potatoes and sweet potatoes) and can distinguish them from similar flowers of relative species (eggplants and Ipomoea triloba) on mobile devices. In this work, a custom dataset containing 495 manually labelled images is constructed for training and testing, and the latest state-of-the-art object detection model, YOLOv4, as well as its lightweight version, YOLOv4-tiny, are selected as the flower detection models. Some other milestone object detection models including YOLOv3, YOLOv3-tiny, SSD and Faster-RCNN are chosen as benchmarks for performance comparison. the comparative experiment results indicate that the retrained YOLOv4 model achieves a considerable high mean average precision (mAP = 91%) but a slower inference speed (FPS) on a mobile device, while the retrained YOLOv4-tiny has a lower mAP of 87% but reach a higher FPS of 9 on a mobile device. Two mobile applications are then developed by directly deploying YOLOv4-tiny model on a mobile app and by deploying YOLOv4 on a web API, respectively. the testing experiments indicate that both applications can not only achieve real-time and accurate detection, but also reduce computation burdens on mobile devices.

关键词： mobile machine learning computer vision object detection YOLOv4

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Incorporating Game-theoretic Rough Sets in Web-based Medical Decision Support Systems

Incorporating Game-theoretic Rough Sets in Web-based Medical...

引用

12th international conference on machine learning and applications (icmla)

作者： Yao, JingTao Azam, Nouman Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada

ISBN: (纸本)9780769551449

Web-based support systems (WSS) assist human activities with the modern Web technology. An important branch of WSS is Web-based decision support systems that provide intelligent support for decision making tasks. We focus on decision making in Web-based medical decision support systems (WMDSS) that can provide support for making diagnosis and treatment decisions. the use of game-theoretic rough set (GTRS) component in WMDSS is explored and investigated for this purpose. the GTRS is a recent development in rough sets that takes advantages from data analysis capabilities of rough sets complimented with decision analysis abilities of game theory. the GTRS may be used to obtain rough sets based three-way or ternary decisions by determining a pair of threshold values. Demonstrative example suggests that the GTRS may be considered as an alternative decision making model and component in building WMDSS for providing decision support.

关键词： Game-thoeretic rough sets decision support probabilistic rough sets rough sets web-based support systems

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A framework for comprehensive electronic QA in radiation therapy

A framework for comprehensive electronic QA in radiation the...

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9th international conference on machine learning and applications, icmla 2010

作者： Kildea, J. Evans, M. Parker, W. Medical Physics Department McGill University Health Centre 1650 avenue Cedar Montreal QC H3G 1A4 Canada

ISBN: (纸本)9780769543000

We describe a framework for comprehensive electronic QA currently under development in the department of Radiation Oncology at the Montreal General Hospital. When complete, the system will incorporate all data generated within the department. It will allow for easy access to all aspects of a patient's treatment and to the state of all relevant equipment at the time of treatment. Quality control will be achieved through automated Shewhart-type control charting with appropriate statistical analysis. machine learning methods will be used to examine the data in order to search for errors, inconsistencies and unnecessary treatment delays. the system will entail a web interface internal to the clinic and written in PERL. © 2010 IEEE.

关键词： Oncology

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