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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是111-120 订阅

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machine learning models and methods aspects of processing unstructured data 1

Machine learning models and methods aspects of processing un...

引用

1st international workshop on Bioinformatics and Applied Information Technologies, BAIT 2024

作者： Bryk, Oleksandr Mudryk, Ivan Holubovskyi, Mykhailo stoianov, Yurii Ternopil Ivan Puluj National Technical University 56 Ruska str. Ternopil46001 Ukraine

The ever-increasing amount of unstructured data, including text, images, audio, and video, poses a serious challenge to traditional data mining techniques. machine learning (ML) offers powerful tools and techniques to unlock the valuable insights hidden in this vast amount of information. This article explores the role of machine learning models and methods in processing unstructured data. We delve into key aspects of unstructured data processing, including data cleaning, feature development, and model selection. We explore specific ML techniques developed for different types of data, such as natural language processing (NLP) for text analysis and computer vision for image recognition. The paper also discusses the challenges and considerations involved in building and deploying ML models to handle unstructured data[1]. By understanding the capabilities of ML on unstructured data, organizations can gain a competitive advantage by deriving valuable insights for various applications. This information can range from understanding customer sentiment in social media posts to detecting anomalies in sensor data for predictive maintenance. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Predictive maintenance

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Advantage and Drawback of Support Vector machine Functionality 1

Advantage and Drawback of Support Vector Machine Functionali...

引用

1st international Conference on Computer, Communications, and Control Technology (I4CT)

作者： Karamizadeh, Sasan Abdullah, Shahidan M. Halimi, Mehran Shayan, Jafar Rajabi, Mohammad Javad UTM AIS Kuala Lumpur Malaysia

ISBN: (纸本)9781479945559

Support Vector machine(SVM) is one of the most efficient machine learning algorithms, which is mostly used for pattern recognition since its introduction in 1990s. SVMs vast variety of usage, such as face and speech recognition, face detection and image recognition has turned it into a very useful algorithm. This has also been applied to many pattern classification problems such as image recognition, speech recognition, text categorization, face detection, and faulty card detection. statistics was collected from journals and electronic sources published in the period of 2000 to 2013. pattern recognition aims to classify data based on either a priori knowledge or statistical information extracted from raw data, which is a powerful tool in data separation in many disciplines. The Support Vector machine ( SVM) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.

关键词： upport Vector machine pattern recognition, classify, and face detection Biometricupport Vector machine pattern recognition, classify, and face detection BiometricS

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Collecting Retail data Using a Deep learning Identification Experience 2nd

Collecting Retail Data Using a Deep Learning Identification ...

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

作者： La Porta, Salvatore Marconi, Fabrizio Lazzini, Isabella JEF Srl Civitanova Marche Italy Consumer Business Group Italia Huawei Milan Italy

ISBN: (纸本)9783030307530

The aim of the paper is to present a part of an architecture realized by Huawei, that propose the first Christmas tree endowed with artificial intelligence. Its ability is to identify facial expressions from images acquired by a mobile application and then recognize the sentiment of the subject. So, basing on the prevailing sentiment the tree lights up itself with different special effects. Our task in the project was testing the performances of the neural networks employed in the mobile application for the recognition of facial emotion. We used a convolutional neural networks model-based and created a purposely dedicated dataset of images for testing the recognition performances. © 2019, Springer Nature Switzerland AG.

关键词： Emotion recognition

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Scaling large learning problems with hard parallel mixtures 1st

Scaling large learning problems with hard parallel mixtures

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1st international workshop on pattern recognition with Support Vector machines

作者： Collobert, R Bengio, Y Bengio, S IDIAP CH-1920 Martigny Switzerland Univ Montreal DIRO Montreal PQ Canada

ISBN: (纸本)354044016X

A challenge for statistical learning is to deal with large data sets, e.g. in data mining. The training time of ordinary Support Vector machines is at least quadratic, which raises a serious research challenge if we want to deal with data sets of millions of examples. We propose a "hard parallelizable mixture" methodology which yields significantly reduced training time through modularization and paxallelization: the training data is iteratively partitioned by a "gater" model in such a way that it becomes easy to learn an "expert" model separately in each region of the partition. A probabilistic extension and the use of a set of generative models allows representing the gater so that all pieces of the model are locally trained. For SVMs, time complexity appears empirically to local growth linearly with the number of examples, while generalization performance can be enhanced. For the probabilistic version of the algorithm, the iterative algorithm probably goes down in a cost function that is an upper bound on the negative log-likelihood.

关键词： mixture of experts support vector machines multilayered perceptrons probabilistic models data-mining large-scale learning dimensionality reduction Gaussian mixtures

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Proceeding of the 1st international workshop on Image mining Theory and Application, IMTA 2008 - In Conjuction with VIISIGRAPP 2008

Proceeding of the 1st International Workshop on Image Mining...

引用

1st international workshop on Image mining Theory and Applications, IMTA 2008 - In Conjunction with VIISIGRAPP 2008

ISBN: (纸本)9789898111258

The proceedings contain 14 papers. The topics discussed include: descriptive analysis of image data: basic models;media analysis and the algorithm ontology;descriptive approach to medical image analysis- substantiation and interpretation;shape modeling for the analysis of heart deformation patterns;fast multi-view evaluation of data represented by symmetric clusters;search algorithm and the distortion analysis of fine details of real images;a proposal for automatic inference of pressure ulcers grade based on wound images and patient data;an image mining medical warehouse;geo-located image categorization and location recognition;pearling: stroke segmentation with crusted pearl strings;automatic target retrieval in a video surveillance task;and learning probabilistic models for recognizing faces under pose variations.

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Image recommendation for Wikipedia articles 1

Image recommendation for Wikipedia articles

引用

1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Onyshchak, Oleh Redi, Miriam Ukrainian Catholic University Lviv Ukraine Wikimedia Foundation London United Kingdom

Multimodal learning, which is simultaneous learning from different data sources such as audio, text, images, is a rapidly emerging field of machine learning. It is also considered as machine learning at the next upper level of abstraction. This allows tackle more complicated problems such as creating cartoons from a plot or speech recognition based on lips movement. In this paper, we propose to research whether state-of-the-art techniques of multimodal learning, will solve the problem of recommending the most relevant images for a Wikipedia article. In other words, we need to create a shared text-image representation of an abstract notion which paper describes, so that having only a text description machine would"understand" which images would visualize the same notion accurately. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： machine learning

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Combining data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma

引用

1st international workshop on Similarity-Based pattern recognition (SIMBAD)

作者： Gonen, Mehmet Ulas, Aydin Schueffler, Peter Castellani, Umberto Murino, Vittorio Aalto Univ Sch Sci Dept Informat & Comp Sci HIIT Espoo Finland Univ Verona Dept Comp Sci Verona Italy ETH Dept Comp Sci Zurich Switzerland IIT Genoa Italy

ISBN: (纸本)9783642244704;9783642244711

In kernel-based machine learning algorithms, we can learn a combination of different kernel functions in order to obtain a similarity measure that better matches the underlying problem instead of using a single fixed kernel function. This approach is called multiple kernel learning (MKL). In this paper, we formulate a nonlinear MKL variant and apply it for nuclei classification in tissue microarray images of renal cell carcinoma (RCC). The proposed variant is tested on several feature representations extracted from the automatically segmented nuclei. We compare our results with single-kernel support vector machines trained on each feature representation separately and three linear MKL algorithms from the literature. We demonstrate that our variant obtains more accurate classifiers than competing algorithms for RCC detection by combining information from different feature representations nonlinearly.

关键词： multiple kernel learning renal cell carcinoma support vector machines

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Online sequential learning based on enhanced extreme learning machine using leftor right pseudo-inverse

Online sequential learning based on enhanced extreme learnin...

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1st international Conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Zong, Weiwei Lan, Yuan Huang, Guang-Bin School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789898425980

The latest development (Huang et al., 2011) has shown that better generalization performance can be obtained for extreme learning machine (ELM) by adding a positive value to the diagonal of HT H or HHT, where H is the hidden layer output matrix. This paper further extends this enhanced ELM to online sequential learning mode. An online sequential learning algorithm is proposed for SLFNs and other regularization networks, consisting of two formulas for two kinds of scenarios: when initial training data is of small scale or large scale. Performance of proposed online sequential learning algorithm is demonstrated through six benchmarking data sets for both regression and multi-class classification problems.

关键词： Benchmarking

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A hierarchical system for recognition, tracking and pose estimation

A hierarchical system for recognition, tracking and pose est...

引用

1st international workshop on machine learning for Multimodal Interaction

作者： Zehnder, P Koller-Meier, E Van Gool, L ETH DITET Comp Vis Lab CH-8092 Zurich Switzerland

ISBN: (纸本)354024509X

This paper presents a new system for recognition, tracking and pose estimation of people in video sequences. It is based on the wavelet transform from the upper body part and uses Support Vector machines (SVM) for classification. recognition is carried out hierarchically by first recognizing people and then individual characters. The characteristic features that best discriminate one person from another are learned automatically. Tracking is solved via a particle filter that utilizes the SVM output and a first order kinematic model to obtain a robust scheme that successfully handles occlusion, different poses and camera zooms. For pose estimation a collection of SVM classifiers is evaluated to detect specific, learned poses.

关键词： pattern recognition

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Extending framenet to machine learning domain 5

Extending framenet to machine learning domain

引用

5th Joint workshop on data mining and Knowledge Discovery meets Linked Open data and the 1st international workshop on Completing and Debugging the Semantic Web, Know@LOD 2016 and CoDeS 2016

作者： Jakubowski, Piotr Lawrynowicz, Agnieszka Institute of Computing Science Poznan University of Technology Poland

In recent years, several ontological resources have been proposed to model machine learning domain. However, they do not provide a direct link to linguistic data. In this paper, we propose a linguistic resource, a set of several semantic frames with associated annotated initial corpus in machine learning domain, we coined MLFrameNet. We have bootstrapped the process of (manual) frame creation by text mining on the set of 1293 articles from the machine learning Journal from about 100 volumes of the journal. It allowed us to find frequent occurences of words and bigrams serving as candidates for lexical units and frame elements. We bridge the gap between linguistics analysis and formal ontologies by typing the frame elements with semantic types from the DMOP domain ontology. The resulting resource is aimed to facilitate tasks such as knowledge extraction, question answering, summarization etc. in machine learning domain.

关键词： Ontology

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