The Experts below are selected from a list of 21123 Experts worldwide ranked by ideXlab platform

J.r. Smith - One of the best experts on this subject based on the ideXlab platform.

  • ICME - Ontology-based multi-Classification Learning for video concept detection
    2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004
    Co-Authors: Yi Wu, B.l. Tseng, J.r. Smith
    Abstract:

    In this paper, an ontology-based multi-Classification Learning algorithm is adopted to detect concepts in the NIST TREC-2003 video retrieval benchmark which defines 133 video concepts, organized hierarchically and each video data can belong to one or more concepts. The algorithm consists of two steps. In the first step, each single concept model is constructed independently. In the second step, ontology-based concept Learning improves the accuracy of the individual concept by considering the possible influence relations between concepts based on a predefined ontology hierarchy. The advantage of ontology Learning is that its influence path is based on an ontology hierarchy, which has real semantic meanings. Besides semantics, it also considers the data correlation to decide the exact influence assigned to each path, which makes the influence more flexible according to data distribution. This Learning algorithm can be used for multiple topic document Classification such as Internet documents and video documents. We demonstrate that precision-recall can be significantly improved by taking ontology into account

  • Ontology-based multi-Classification Learning for video concept detection
    2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004
    Co-Authors: Yijian Wu, J.r. Smith
    Abstract:

    In this paper, an ontology-based multi-Classification Learning algorithm is adopted to detect concepts in the NIST TREC-2003 video retrieval benchmark which defines 133 video concepts, organized hierarchically and each video data can belong to one or more concepts. The algorithm consists of two steps. In the first step, each single concept model is constructed independently. In the second step, ontology-based concept Learning improves the accuracy of the individual concept by considering the possible influence relations between concepts based on a predefined ontology hierarchy. The advantage of ontology Learning is that its influence path is based on an ontology hierarchy, which has real semantic meanings. Besides semantics, it also considers the data correlation to decide the exact influence assigned to each path, which makes the influence more flexible according to data distribution. This Learning algorithm can be used for multiple topic document Classification such as Internet documents and video documents. We demonstrate that precision-recall can be significantly improved by taking ontology into account

E. Herrera-viedma - One of the best experts on this subject based on the ideXlab platform.

  • Using multi-granular fuzzy linguistic modelling methods for supervised Classification Learning purposes
    2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
    Co-Authors: J. A. Morente-molinera, J. Mezei, C. Carlsson, E. Herrera-viedma
    Abstract:

    Classification Learning is a very complex process whose success and failure ratio depends on a high amount of elements. One of them is the representation mean used for the data that is employed in the process. Granularity of the data used for Classification Learning purposes can affect dramatically the success and failure ratio of the obtained Classification. In this paper, multi-granular fuzzy linguistic modelling methods are applied over the Classification Learning data in order to modify their granularity and increase the Classification success ratio. Thanks to multi-granular fuzzy linguistic modelling methods, it is possible to automatically modify the data granularity in order to determine which data representation is the one that provides the better Classification results in the Learning process.

  • FUZZ-IEEE - Using multi-granular fuzzy linguistic modelling methods for supervised Classification Learning purposes
    2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017
    Co-Authors: J. A. Morente-molinera, J. Mezei, C. Carlsson, E. Herrera-viedma
    Abstract:

    Classification Learning is a very complex process whose success and failure ratio depends on a high amount of elements. One of them is the representation mean used for the data that is employed in the process. Granularity of the data used for Classification Learning purposes can affect dramatically the success and failure ratio of the obtained Classification. In this paper, multi-granular fuzzy linguistic modelling methods are applied over the Classification Learning data in order to modify their granularity and increase the Classification success ratio. Thanks to multi-granular fuzzy linguistic modelling methods, it is possible to automatically modify the data granularity in order to determine which data representation is the one that provides the better Classification results in the Learning process.

Thomas Villmann - One of the best experts on this subject based on the ideXlab platform.

  • ICAISC (1) - Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning
    Artificial Intelligence and Soft Computing, 2018
    Co-Authors: Andrea Villmann, M. Kaden, Sascha Saralajew, Thomas Villmann
    Abstract:

    Classification Learning by prototype based approaches is an attractive strategy to achieve interpretable Classification models. Frequently, those models optimize the Classification error or an approximation thereof. Current deep network approaches use the cross entropy maximization instead. Therefore, we propose a prototype based classifier based on cross-entropy as a probabilistic classifier. As we deduce, the proposed probabilistic classifier is a generalization of the robust soft-Learning vector quantizer and allows to handle label noise in training data, i.e. the classifier is able to take into account probabilistic class assignments during Learning.

  • Types of (dis-)similarities and adaptive mixtures thereof for improved Classification Learning
    Neurocomputing, 2017
    Co-Authors: D. Nebel, M. Kaden, Andrea Villmann, Thomas Villmann
    Abstract:

    Abstract In this paper, we introduce taxonomies for similarity and dissimilarity measures, respectively, based on their mathematical properties. Further, we propose a definition for rank equivalence of (dis)similarities regarding given data for prototype based methods. Starting with this definition we provide a measure to judge the degree of equivalence, which can be used to compare respective measures as well as to consider the influence of data preprocessing regarding a single (dis)similarity measure. In the last part of the paper an adaptive mixture approach of (dis)similarity measures for improved Classification Learning is presented.

  • ICONIP (3) - Adaptive Hausdorff Distances and Tangent Distance Adaptation for Transformation Invariant Classification Learning
    Neural Information Processing, 2016
    Co-Authors: Sascha Saralajew, D. Nebel, Thomas Villmann
    Abstract:

    Tangent distances (TDs) are important concepts for data manifold distance description in machine Learning. In this paper we show that the Hausdorff distance is equivalent to the TD for certain conditions. Hence, we prove the metric properties for TDs. Thereafter, we consider those TDs as dissimilarity measure in Learning vector quantization (LVQ) for Classification Learning of class distributions with high variability. Particularly, we integrate the TD in the Learning scheme of LVQ to obtain a TD adaption during LVQ Learning. The TD approach extends the classical prototype concept to affine subspaces. This leads to a high topological richness compared to prototypes as points in the data space. By the manifold theory of TDs we can ensure that the affine subspaces are aligned in directions of invariant transformations with respect to class discrimination. We demonstrate the superiority of this new approach by two examples.

  • Adaptive tangent distances in generalized Learning vector quantization for transformation and distortion invariant Classification Learning
    2016 International Joint Conference on Neural Networks (IJCNN), 2016
    Co-Authors: Sascha Saralajew, Thomas Villmann
    Abstract:

    We propose a Learning vector quantization algorithm variant for prototype-based Classification Learning with adaptive tangent distance Learning. Tangent distances were developed to achieve dissimilarity measures invariant with respect to transformations and distortions like rotation, noise, etc.. Usually, these tangent distances are predefined in applications or are estimated in preprocessing. We introduce in this paper a generalized Learning vector quantizer (GLVQ) with an online adaptation scheme for tangent distances. The adaptation takes place as a stochastic gradient descent Learning accompanying the usual online prototype Learning. In this way, class discriminative tangents are learned contributing to a better Classification performance. Further, the resulting update schemes can be seen as a special type of local matrix Learning in GLVQ. In this paper, we provide the full mathematical theory behind the derived tangent distance adaptation rule and demonstrate the Classification ability of the resulting GLVQ model in comparison to state-of-the-art tangent distance based classifiers in the field.

  • Aspects in Classification Learning - Review of recent developments in Learning vector quantization
    Foundations of Computing and Decision Sciences, 2014
    Co-Authors: M. Kaden, D. Nebel, T. Geweniger, Martin Riedel, M Lange, Thomas Villmann
    Abstract:

    Classification is one of the most frequent tasks in machine Learning. However, the variety of Classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in Classification Learning. This paper focuses on Learning vector quantization classifiers as one of the most intuitive prototype based Classification models. Recent extensions and modifications of the basic Learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular Classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, Classification guarantees or adaptive data metrics for optimal Classification.

Xinyi Le - One of the best experts on this subject based on the ideXlab platform.

  • Clustering-enhanced PointCNN for Point Cloud Classification Learning
    2019 International Joint Conference on Neural Networks (IJCNN), 2019
    Co-Authors: Yikuan Yu, Fei Li, Yu Zheng, Xinyi Le
    Abstract:

    3D shape feature Learning plays a pivotal role in both industry and academia. PointCNN is one of excellent neural networks for 3D object databases Classification. Instead of selecting representative points arbitrarily in PointCNN, clustering-enhanced PointCNN proposed in this paper can make representative points more logical and efficient for point cloud Classification Learning. The proposed clustering-based selection approach is able to distinguish more features and catch more details from 3D shapes. Both K-Means and Gaussian-Mixture-Model (GMM) clustering methods are applied during the point selection period. Both methods have been tested on several public data sets, which substantiates the superior Classification accuracy with comparable training time.

  • IJCNN - Clustering-enhanced PointCNN for Point Cloud Classification Learning
    2019 International Joint Conference on Neural Networks (IJCNN), 2019
    Co-Authors: Yikuan Yu, Fei Li, Yu Zheng, Xinyi Le
    Abstract:

    3D shape feature Learning plays a pivotal role in both industry and academia. PointCNN is one of excellent neural networks for 3D object databases Classification. Instead of selecting representative points arbitrarily in PointCNN, clustering-enhanced PointCNN proposed in this paper can make representative points more logical and efficient for point cloud Classification Learning. The proposed clustering-based selection approach is able to distinguish more features and catch more details from 3D shapes. Both K-Means and Gaussian-Mixture-Model (GMM) clustering methods are applied during the point selection period. Both methods have been tested on several public data sets, which substantiates the superior Classification accuracy with comparable training time.

Yijian Wu - One of the best experts on this subject based on the ideXlab platform.

  • Ontology-based multi-Classification Learning for video concept detection
    2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004
    Co-Authors: Yijian Wu, J.r. Smith
    Abstract:

    In this paper, an ontology-based multi-Classification Learning algorithm is adopted to detect concepts in the NIST TREC-2003 video retrieval benchmark which defines 133 video concepts, organized hierarchically and each video data can belong to one or more concepts. The algorithm consists of two steps. In the first step, each single concept model is constructed independently. In the second step, ontology-based concept Learning improves the accuracy of the individual concept by considering the possible influence relations between concepts based on a predefined ontology hierarchy. The advantage of ontology Learning is that its influence path is based on an ontology hierarchy, which has real semantic meanings. Besides semantics, it also considers the data correlation to decide the exact influence assigned to each path, which makes the influence more flexible according to data distribution. This Learning algorithm can be used for multiple topic document Classification such as Internet documents and video documents. We demonstrate that precision-recall can be significantly improved by taking ontology into account