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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51-60 订阅

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Convolutional Neural Support Vector machines: Hybrid Visual Pattern Classifiers for Multi-robot Systems

Convolutional Neural Support Vector Machines: Hybrid Visual ...

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11th IEEE international conference on machine learning and applications (icmla)

作者： Nagi, Jawad Di Caro, Gianni A. Giusti, Alessandro Nagi, Farrukh Gambardella, Luca M. Dalle Molle Inst Artificial Intelligence IDSIA Lugano Switzerland Univ Tenaga Nas Putrajaya Malaysia

ISBN: (纸本)9780769549132;9781467346511

We introduce Convolutional Neural Support Vector machines (CNSVMs), a combination of two heterogeneous supervised classification techniques, Convolutional Neural Networks (CNNs) and Support Vector machines (SVMs). CNSVMs are trained using a Stochastic Gradient Descent approach, that provides the computational capability of online incremental learning and is robust for typical learning scenarios in which training samples arrive in mini-batches. this is the case for visual learning and recognition in multi-robot systems, where each robot acquires a different image of the same sample. the experimental results indicate that the CNSVM can be successfully applied to visual learning and recognition of hand gestures as well as to measure learning progress.

关键词： Convolutional Neural Networks Multi-robot Systems Supervised learning Support Vector machines Swarm Robotics

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Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features

Predicting Algorithm Accuracy with a Small Set of Effective ...

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7th international conference on machine learning and applications

作者： Lee, Jun won Giraud-Carrier, Christophe Brigham Young Univ Dept Comp Sci Provo UT 84602 USA

ISBN: (纸本)9780769534954

We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-features whose values are directly relevant to certain ranges of predictive accuracy for 7 learning algorithms on 135 UCI datasets. Discretization of these 4 meta-features based on thresholds derived from our analysis significantly boosts the accuracy of the meta-level classification task.

关键词： learning algorithms

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Optimal Tile Size Selection Problem using machine learning

Optimal Tile Size Selection Problem using Machine Learning

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11th IEEE international conference on machine learning and applications (icmla)

作者： Malik, Abid M. Univ Houston Dept Comp Sci Houston TX 77004 USA

ISBN: (纸本)9780769549132

One of the key feature of modern architectures is deep memory hierarchies. In order to exploit this feature, one has to expose data locality with-in a program. Loop tiling is an optimization phase in modern compilers which is used to transform a loop for exposing data locality. Selecting the best tile size for a given architecture and compiler is known as Optimal Tile Size Selection Problem. It is a NP-hard problem. People have build cost models for this problem that characterize the performance of a program as a function of tile sizes. the best tile size for a given loop is determined directly by using these models. Hand crafting an accurate tile size selection cost model is hard. Can we automatically learn a tile size selection model? this is an importnat question. In this paper, we have shown that a fairly accurate model can be learned using simple program dynamic features with standard machine learning techniques. We evaluate our approach on different architecture and compiler combinations. the model given by us consistently shows near-optimal performance (within 4% of the optimal) across all architecture and compiler combinations.

关键词： machine learning tile size selection loop tiling loop blocking compiler optimization artificial neural networks

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A fast feature selection model for online handwriting symbol recognition A fast feature selection model for online handwriting symbol recognition

A fast feature selection model for online handwriting symbol...

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5th international conference on machine learning and applications

作者： Huang, B. Q. Kechadi, M-T. Natl Univ Ireland Univ Coll Dublin Sch Comp Sci & Informat Dublin 4 Ireland

ISBN: (纸本)9780769527352

Many feature selection models have been proposed for online handwriting recognition. However most of them require expensive computational overhead, or inaccurately find an improper feature set which leads to unacceptable recognition rates. this paper presents a new efficient feature selection model for handwriting symbol recognition by using an improved sequential floating search method coupled with a hybrid classifier which is obtained by combining Hidden Markov Models with Multilayer Forward Network. the effectiveness of proposed method is verified by comprehensive experiments based on UNIPEN database.

关键词： Feature extraction

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Multiple Imputation of Missing Residuals for Fault Classification: a Wind Turbine Application 14

Multiple Imputation of Missing Residuals for Fault Classific...

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IEEE 14th international conference on machine learning and applications icmla

作者： Nejad, Eman M. Razavi-Far, Roozbeh Wu, Q. M. Jonathan Saif, Mehrdad Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781509002870

Handling the missing data is considered as a crucial requirement for the performance of diagnostic systems. In the proposed diagnostic system, the preprocessing module receives sets of residuals generated by a combined set of observers, and feeds the proceeded residuals to a fault classification module. It is necessary for the fault classification module to receive complete feature sets. Multiple missing data imputation techniques have been devised in the preprocessing module to guarantee feeding complete sets of features to the fault classification module. the proposed diagnostic scheme is validated using incomplete batch of residuals for sensor fault diagnosis in a doubly fed induction generator (DFIG) of a wind turbine.

关键词： multiple imputation fault diagnosis sensor faults wind turbine

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On a distributed approach for density-based clustering

On a distributed approach for density-based clustering

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10th international conference on machine learning and applications, icmla 2011

作者： Le-Khac, Nhien-An Kechadi, M-Tahar School of Computer Science University College Dublin Belfield Dublin 4 Ireland

ISBN: (纸本)9780769546070

Efficient extraction of useful knowledge from very large datasets is still a challenge, mainly when the datasets are distributed, heterogeneous and of different quality depending of the various nodes involved. To reduce the overhead cost due to communications, most of the existing distributed clustering approaches generates global models by aggregating local results obtained on each individual node. the complexity and quality of solutions depend highly on the quality of the aggregation. In this respect, we propose distributed density-based clustering that both reduces the communication overheads and improves the quality of the global models by considering the shapes of local clusters. From preliminary results we show that this algorithm is very promising. © 2011 IEEE.

关键词： Cost reduction

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learning Multi-Valued Biological Models with Delayed Influence from Time-Series Observations 14

Learning Multi-Valued Biological Models with Delayed Influen...

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IEEE 14th international conference on machine learning and applications icmla

作者： Ribeiro, Tony Magnin, Morgan Inoue, Katsumi Sakama, Chiaki SOKENDAI Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan Ecole Cent Nantes Inst Rech Commun & Cybernet Nantes IRCCyN F-44321 Nantes France Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan Wakayama Univ Wakayama 6408510 Japan

ISBN: (纸本)9781509002870

Delayed effects are important in modeling biological systems, and timed Boolean networks have been proposed for such a framework. Yet it is not an easy task to design such Boolean models with delays precisely. Recently, an attempt to learn timed Boolean networks has been made in [1] in the framework of learning state transition rules from time-series data. However, this approach still has two limitations: (1) the maximum delay has to be given as input to the algorithm;(2) the possible value of each state is assumed to be Boolean, i.e., two-valued. In this paper, we extend the previous learning mechanism to overcome these limitations. We propose an algorithm to learn multi-valued biological models with delayed influence by automatically tuning the delay. the delay is determined so as to minimally explain the necessary influences. the merits of our approach is then verified on benchmarks coming from the DREAM4 challenge.

关键词： dynamical systems multi-valued models Boolean networks delay Markov(k) learning from interpretation transition inductive logic programming

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machine learning techniques for LV wall motion classification based on spatio-temporal profiles from cardiac cine MRI

Machine learning techniques for LV wall motion classificatio...

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

作者： Mantilla, Juan Garreau, Mireille Bellanger, Jean-Jacques Paredes, Jose Luis INSERM U1099 F-35000 Rennes France Univ Rennes 1 LTSI F-35000 Rennes France Univ Nacl Expt Tachira Decanato Invest Grp Bioingn San Cristobal Venezuela Univ Los Andes Ctr Biomed Engn & Telemed CIByTEL Merida Venezuela

ISBN: (纸本)9780769551449

In this paper, we propose an automated method to classify normal/abnormal wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI). Without the need of pre-processing and by exploiting all the images of a cardiac cycle, spatio-temporal profiles are extracted from a subset of diametrical lines crossing opposites segments of the ventricular cavity. Two machine learning techniques are adapted and tested. the first one, is based on classical Support Vector machines (SVM) and the second one that is proposed is based on dictionary learning (DL), adapted for classification in a supervised learning fashion. the experiments are evaluated based on features extracted from gray levels of the spatio-temporal profile as well as their representations in other basis such as Fourier and Wavelet domains under the assumption that the data may be sparse in one of those domains. the best classification performance has been obtained with a three-level db4 2-Dimensional wavelet transform using Fisher Discriminative Dictionary learning as technique of classification.

关键词： Dictionary learning Left Ventricle motion Support Vector machine cardiac cine-Magnetic Resonance Imaging Support Vector Network Cine Magnetic Resonance Imaging Taxonomy Left ventricular wall motion Opposites learning methods Artificial Intelligence machine learning gray level Automated methods Heart Dictionaries as Topic

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Basic investigation on a robust and practical plant diagnostic system 15

Basic investigation on a robust and practical plant diagnost...

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

作者： Fujita, Erika Kawasaki, Yusuke Uga, Hiroyuki Kagiwada, Satoshi Iyatomi, Hitoshi Hosei Univ Appl Informat Grad Sch Sci & Engn Tokyo Japan Saitama Agr Technol Res Ctr Saitama Japan Hosei Univ Clin Plant Sci Fac Biosci & Appl Chem Tokyo Japan

ISBN: (纸本)9781509061679

Accurate plant diagnosis requires experts' knowledge but is usually expensive and time consuming. therefore, it has become necessary to design an accurate, easy, and low-cost automated diagnostic system for plant diseases. In this paper, we propose a new practical plant-disease detection system. We use 7,520 cucumber leaf images comprising images of healthy leaves and those infected by almost all types of viral diseases. the leaves were photographed on site under only one requirement, that is, each image must contain a leaf roughly at its center, thus providing them with a large variety of appearances (i.e., parameters including distance, angle, background, and lighting condition were not uniform). Although half of the images used in this experiment were taken in bad conditions, our classification system based on convolutional neural networks attained an average of 82.3% accuracy under the 4-fold cross validation strategy.

关键词： deep learning plant disease convolutional neural networks

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Using Classification in the Preprocessing Step on Wi-Fi Data as an Enabler of Physical Analytics 15

Using Classification in the Preprocessing Step on Wi-Fi Data...

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

作者： Sarshar, Hossein Matwin, Stan Dalhousie Univ Fac Comp Sci 6050 Univ Ave Halifax NS B3H 4R2 Canada Polish Acad Sci Inst Comp Sci Warsaw Poland Inst Big Data Analyt Halifax NS B3H 4R2 Canada

ISBN: (纸本)9781509061679

In this research we present a remote localization technique as an essential preprocessing step to enable Physical Analytics in the retail and hospitality sector. We studied two crowdsourced Wi-Fi data sources as potential inputs for fingerprinting-based positioning systems. these sources are non intrusively crowdsourced and can be easily acquired at almost any retail store. We evaluated our hypothesis on large, real-world datasets using statistical and machine learning techniques. With the use of these sources, we built a fingerprinting-based positioning system that achieved reliable and accurate physical positioning results. Our method is capable of estimating positions without any prior knowledge about the store plan or the antennas' location with, only one off-the-shelf access point. Unlike other positioning techniques, instead of estimating a relative position of a device from an antenna, we provide an absolute position for a device as inside or outside of a venue without making any assumption about the site nor the positioned devices. To investigate its practicality, we evaluated our method with datasets of five different stores.

关键词： physical analytics Wi-Fi indoor localization crowdsourcing supervised learning

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