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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211-220 订阅

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Novel Supervised Feature Extraction Algorithm Based on Iterative Calculations

Novel Supervised Feature Extraction Algorithm Based on Itera...

引用

12th IEEE international Conference on Information Reuse and Integration (IRI)/1st international workshop on Issues and Challenges in Social Computing (WICSOC)

作者： Takeuchi, Yohei Ito, Momoyo Kashihara, Koji Fukumi, Minoru Univ Tokushima Grad Sch Adv Technol & Sci Tokushima 770 Japan

ISBN: (纸本)9781457709661

In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. Thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.

关键词： pattern recognition principal component analysis supervised learning

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OPTIMAL ROBUstNESS IN NONITERATIVE learning

OPTIMAL ROBUSTNESS IN NONITERATIVE LEARNING

引用

1st international Conference on Applications and Science of Artificial Neural Networks

作者： HU, CLJ SO ILLINOIS UNIV DEPT ELECT ENGNCARBONDALEIL 62901

ISBN: (纸本)0819418455

If M given training patterns are not extremely similar, the analog N-vectors representing them are generally separable in the N-space. Then a one-layered binary perceptron containing P neurons (P equals >log₂M) is generally sufficient to do the pattern recognition job. The connection matrix between the input (linear) layer and the neuron layer can be calculated in a noniterative manner. Real-time pattern recognition experiments implementing this theoretical result were reported in this and other national conferences last year. It is demonstrated in these experiments that the noniterative training is very fast, (can be done in real time), and the recognition of the untrained patterns is very robust and very accurate. The present paper concentrates at the theoretical foundation of this noniteratively trained perceptron. The theory starts from an N-dimension Euclidean-geometry approach. An optimally robust learning scheme is then derived. The robustness and the speed of this optimal learning scheme are to be compared with those of the conventional iterative learning schemes.

关键词： pattern recognition Radon Binary data Neurons Analog electronics Lithium machine learning Electrical engineering Matrices Neural networks

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Video shot segmentation using graph-based dominant-set clustering 09

Video shot segmentation using graph-based dominant-set clust...

引用

1st international Conference on Internet Multimedia Computing and Service, ICIMCS 2009

作者： Li, Li Zeng, Xianglin Li, Xi Hu, Weiming Zhu, Pengfei National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781605588407

Video shot segmentation is a solid foundation for automatic video content analysis, for most content based video retrieval tasks require accurate segmentation of video boundaries. In recent years, using the tools of data mining and machine learning to detect shot boundaries has become more and more popular. In this paper, we propose an effective video segmentation approach based on a dominant-set clustering algorithm. The algorithm can not only automatically determine the number of video shots, but also obtain accurate shot boundaries with low computation complexity. Experimental results have demonstrated the effectiveness of the proposed shot segmentation approach. Copyright 2009 ACM.

关键词： Clustering algorithms

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Efficient Unsupervised learning for Plankton Images 26

Efficient Unsupervised Learning for Plankton Images

引用

26th international Conference on pattern recognition / 8th international workshop on Image mining - Theory and Applications (IMTA)

作者： Alfano, Paolo Didier Rando, Marco Letizia, Marco Odone, Francesca Rosasco, Lorenzo Pastore, Vito Paolo Univ Genoa MaLGa DIBRIS Genoa Italy INFN Sez Genova Genoa Italy Italian Inst Technol Genoa Italy MIT CBMM 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical modifications. Nowadays, the availability of advanced automatic or semi-automatic acquisition systems has been allowing the production of an increasingly large amount of plankton image data. The adoption of machine learning algorithms to classify such data may be affected by the significant cost of manual annotation, due to both the huge quantity of acquired data and the numerosity of plankton species. To address these challenges, we propose an efficient unsupervised learning pipeline to provide accurate classification of plankton microorganisms. We build a set of image descriptors exploiting a two-step procedure. First, a Variational Autoencoder (VAE) is trained on features extracted by a pre-trained neural network. We then use the learnt latent space as image descriptor for clustering. We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images. The proposed pipeline outperforms the benchmark algorithms for all the plankton datasets included in our analysis, providing better image embedding properties.

关键词： Image segmentation Microorganisms Pipelines Neural networks Sociology Computer architecture Feature extraction

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2021 1st international Conference on Artificial Intelligence and data Analytics, CAIDA 2021

2021 1st International Conference on Artificial Intelligence...

引用

1st international Conference on Artificial Intelligence and data Analytics, CAIDA 2021

ISBN: (纸本)9780738131771

The proceedings contain 54 papers. The topics discussed include: an intelligent approach for food recipe rating prediction using machine learning;hardware implementation of IP-enabled wireless sensor network using 6LoWPAN;an efficient machine learning-based approach for android v.11 ransomware detection;robotics to enhance the teaching and learning process;an efficient pattern recognition based method for drug-drug interaction diagnosis;enhancing the prediction of MERS-CoV survivability using stacking-based method;a new solution to the brain state permanency for brain-based authentication methods;towards efficient detection and crowd management for law enforcing agencies;and AI support marketing: understanding the customer journey towards the business development.

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CEUR workshop Proceedings

CEUR Workshop Proceedings

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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

The proceedings contain 9 papers. The topics discussed include: extending FrameNet to machine learning domain;PageRank on Wikipedia: towards general importance scores for entities;learning semantic rules for intelligent transport scheduling in hospitals;the linked data mining challenge 2016;not-so-linked solution to the linked data mining challenge 2016;a hybrid method for rating prediction using linked data features and text reviews;can you judge a music album by its cover?;finding and avoiding bugs in enterprise ontologies;and a two-fold quality assurance approach for dynamic knowledge bases: the 3cixty use case.

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Survey on Medical Diagnosis Using data mining Techniques

Survey on Medical Diagnosis Using Data Mining Techniques

引用

1st international Conference on Optical Imaging Sensor and Security (ICOSS)

作者： Sumalatha, G. Muniraj, N. J. R. SKASC Dept ICT Coimbatore Tamil Nadu India Tejaa Shakthi Inst Technol Women Coimbatore Tamil Nadu India

ISBN: (纸本)9781479909353;9781467361293

In the nonexistence of medical diagnosis substantiations, it is complicated for the expertto speak out about the grade of disease with affirmation. Generally many tests are done that involve clustering or classification of large scale data However many tests could complicate the main diagnosis process and lead to the difficulty in obtaining the end results, particularly in the case where many tests are performed This kind of difficulty could be resolved with the aid of machine learning techniques. In this paper survey on three different disease diagnosis are taken in to the consideration. The heart Disease, Breast Cancer Disease and the Diabetes Disease are analyzed and observed with existing works. This survey paper reveals various existing approaches that have processed for diagnosis these diseases using data mining techniques.

关键词： cancer cardiology data mining learning (artificial intelligence) medical diagnostic computing patient diagnosis pattern classification pattern clustering

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Web usage mining for automatic link generation

Web usage mining for automatic link generation

引用

10th international workshop on Modelling, Simulation, Verification and Validation of Enterprise Information Systems, MSVVEIS 2012 and 1st international workshop on Web Intelligence, WEBI 2012, in Conjunction with ICEIS 2012

作者： Arbelaitz, Olatz Gurrutxaga, Ibai Lojo, Aizea Muguerza, Javier Pérez, Jesús M. Perona, Iñigo Dept. of Computer Architecture and Technology University of the Basque Country UPV-EHU M. Lardizabal 1 20018 Donostia Spain

ISBN: (纸本)9789898565143

During the last decades, the information in the web has increased drastically but larger quantities of data do not provide perse added value for web visitors;there is a need for more efficient access to the required information and adaptation to user preferences or needs. The use of machine learning techniques to build user profiles allows to take into account users' real preferences. We present in this work a preliminary system, based on the collaborative filtering approach, to identify and generate interesting links for the users while they are navigating. The system uses only web navigation logs stored in any web server (according to the Common Log Format) and extracts information from them combining unsupervised and supervised classification techniques and frequent pattern mining techniques. It also includes a generalization procedure in the data preprocessing phase and in this work we analyze its effect on the final performance of the whole system. We also analyze the effect of the cold start (0 day problem) in the proposed system. The experiments show that the proposed generalization option improves the results of the designed system, which performs efficiently w.r.t. a web-accessible database and is even able to deal with the cold start problem.

关键词： Collaborative filtering

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Feature learning by Least Generalization 1

引用

30th international Conference on Inductive Logic Programming (ILP) held as part of the 1st international Joint Conference on learning and Reasoning (IJCLR)

作者： Nguyen, Hien D. Sakama, Chiaki Univ Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Wakayama Univ Wakayama Japan

ISBN: (数字)9783030974541

ISBN: (纸本)9783030974541;9783030974534

This paper provides an empirical study for feature learning based on induction. We encode image data into first-order expressions and compute their least generalization. An interesting question is whether the least generalization can extract a common pattern of input data. We introduce three different methods for feature extraction based on symbolic manipulation. We perform experiments using the MNIst datasets and show that the proposed methods successfully capture features from training data and classify test data in around 90% accuracies. The results of this paper show potentials of induction and symbolic reasoning to feature learning or pattern recognition from raw data.

关键词： Feature learning Least generalization Interpretable machine learning

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Role of machine learning in Acute stroke Imaging: A Technical Review 1

Role of Machine Learning in Acute Stroke Imaging: A Technica...

引用

1st international Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Singh, Gurmeet Singh, Mahender Gupta, Akansha Singh, Deepjyot Department of Electronics and Communication Engineering New Delhi India Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350367720

The utilization of machine learning (ML) and pattern recognition algorithms, which is used for analysis of complex datasets to detect similarities, differences, or trends, which can be particularly useful in medical diagnostics, has become essential in enhancing the prognostics of various neurological diseases such as Acute Ischemic stroke (AIS). These advanced techniques have particularly transformed the evaluation and exploitation of neuroimaging data in the assessment and management of Acute Ischemic stroke (AIS). In addition, these techniques also enable rapid and accurate analysis of complex imaging data, aiding clinicians in timely diagnosis, personalized treatment planning, and outcome prediction for stroke patients. This paper focuses on a comprehensive examination of machine learning algorithm strategies to enhance the accuracy and effectiveness of predicting strokes. The author has explored and reviewed different ML algorithms and has also delved into stroke prevention to advance our understanding. © 2024 IEEE.

关键词： Adversarial machine learning

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