Relevant bibliographies by topics / Multi-category classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multi-category classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Multi-category classification"

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Li, Luoqing, Chuanwu Yang, and Qiwei Xie. "1D embedding multi-category classification methods." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 02 (March 2016): 1640006. http://dx.doi.org/10.1142/s0219691316400063.

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In this paper, we propose a novel semi-supervised multi-category classification method based on one-dimensional (1D) multi-embedding. Based on the multiple 1D embedding based interpolation technique, we embed the high-dimensional data into several different 1D manifolds and perform binary classification firstly. Then we construct the multi-category classifiers by means of one-versus-rest and one-versus-one strategies separately. A weight strategy is employed in our algorithm for improving the classification performance. The proposed method shows promising results in the classification of handwritten digits and facial images.
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Zhong, Jincheng, and Shuhui Chen. "Efficient multi-category packet classification using TCAM." Computer Communications 169 (March 2021): 1–10. http://dx.doi.org/10.1016/j.comcom.2020.12.027.

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Paek, E. G., J. R. Wullert II, and J. S. Patel. "Optical learning machine for multi-category classification." Optics News 15, no. 12 (December 1, 1989): 28. http://dx.doi.org/10.1364/on.15.12.000028.

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R Udaya Prakash, Muthukrishnan. "Designing a better Support Vector Machines Classification Model for Multi-Class Category." International Journal of Science and Research (IJSR) 12, no. 2 (February 5, 2023): 905–9. http://dx.doi.org/10.21275/sr22517170244.

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Hill, S. I., and A. Doucet. "A Framework for Kernel-Based Multi-Category Classification." Journal of Artificial Intelligence Research 30 (December 12, 2007): 525–64. http://dx.doi.org/10.1613/jair.2251.

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A geometric framework for understanding multi-category classification is introduced, through which many existing 'all-together' algorithms can be understood. The structure enables parsimonious optimisation, through a direct extension of the binary methodology. The focus is on Support Vector Classification, with parallels drawn to related methods. The ability of the framework to compare algorithms is illustrated by a brief discussion of Fisher consistency. Its utility in improving understanding of multi-category analysis is demonstrated through a derivation of improved generalisation bounds. It is also described how this architecture provides insights regarding how to further improve on the speed of existing multi-category classification algorithms. An initial example of how this might be achieved is developed in the formulation of a straightforward multi-category Sequential Minimal Optimisation algorithm. Proof-of-concept experimental results have shown that this, combined with the mapping of pairwise results, is comparable with benchmark optimisation speeds.
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Tan, Jie, Xiaomin Chen, Guansheng Du, Qiaohui Luo, Xiao Li, Yaqing Liu, Xiao Liang, and Jianmin Wu. "Multi-dimensional on-particle detection technology for multi-category disease classification." Chemical Communications 52, no. 17 (2016): 3490–93. http://dx.doi.org/10.1039/c5cc09419d.

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Su, Hao, Zhiping Lin, and Lei Sun. "Extraction of category orthonormal subspace for multi-class classification." Journal of the Franklin Institute 358, no. 9 (June 2021): 5089–112. http://dx.doi.org/10.1016/j.jfranklin.2021.03.029.

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Liu, Defu, Jiayi Zhao, Jinzhao Wu, Guowu Yang, and Fengmao Lv. "Multi-category classification with label noise by robust binary loss." Neurocomputing 482 (April 2022): 14–26. http://dx.doi.org/10.1016/j.neucom.2022.01.031.

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Kang, Jianhong. "Earthquake Information Tracking Based on the Multi-category Classification Method." Journal of Information and Computational Science 12, no. 7 (May 1, 2015): 2647–53. http://dx.doi.org/10.12733/jics20105808.

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Shi, Wei, Yanghe Feng, Guangquan Cheng, Shixuan Liu, and Zhong Liu. "Multi-category Classification Problem Oriented Subsampling-Based Active Learning Method." Journal of Physics: Conference Series 1631 (September 2020): 012003. http://dx.doi.org/10.1088/1742-6596/1631/1/012003.

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Dissertations / Theses on the topic "Multi-category classification"

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Musayeva, Khadija. "Generalization Performance of Margin Multi-category Classifiers." Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0096.

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Cette thèse porte sur la théorie de la discrimination multi-classe à marge. Elle a pour cadre la théorie statistique de l’apprentissage de Vapnik et Chervonenkis. L’objectif est d’établir des bornes de généralisation possédant une dépendances explicite au nombre C de catégories, à la taille m de l’échantillon et au paramètre de marge gamma, lorsque la fonction de perte considérée est une fonction de perte à marge possédant la propriété d’être lipschitzienne. La borne de généralisation repose sur la performance empirique du classifieur ainsi que sur sa "capacité". Dans cette thèse, les mesures de capacité considérées sont les suivantes : la complexité de Rademacher, les nombres de recouvrement et la dimension fat-shattering. Nos principales contributions sont obtenues sous l’hypothèse que les classes de fonctions composantes calculées par le classifieur ont des dimensions fat-shattering polynomiales et que les fonctions composantes sont indépendantes. Dans le contexte du schéma de calcul introduit par Mendelson, qui repose sur les relations entre les mesures de capacité évoquées plus haut, nous étudions l’impact que la décomposition au niveau de l’une de ces mesures de capacité a sur les dépendances (de la borne de généralisation) à C, m et gamma. En particulier, nous démontrons que la dépendance à C peut être considérablement améliorée par rapport à l’état de l’art si la décomposition est reportée au niveau du nombre de recouvrement ou de la dimension fat-shattering. Ce changement peut affecter négativement le taux de convergence (dépendance à m), ce qui souligne le fait que l’optimisation par rapport aux trois paramètres fondamentaux se traduit par la recherche d’un compromis
This thesis deals with the theory of margin multi-category classification, and is based on the statistical learning theory founded by Vapnik and Chervonenkis. We are interested in deriving generalization bounds with explicit dependencies on the number C of categories, the sample size m and the margin parameter gamma, when the loss function considered is a Lipschitz continuous margin loss function. Generalization bounds rely on the empirical performance of the classifier as well as its "capacity". In this work, the following scale-sensitive capacity measures are considered: the Rademacher complexity, the covering numbers and the fat-shattering dimension. Our main contributions are obtained under the assumption that the classes of component functions implemented by a classifier have polynomially growing fat-shattering dimensions and that the component functions are independent. In the context of the pathway of Mendelson, which relates the Rademacher complexity to the covering numbers and the latter to the fat-shattering dimension, we study the impact that decomposing at the level of one of these capacity measures has on the dependencies on C, m and gamma. In particular, we demonstrate that the dependency on C can be substantially improved over the state of the art if the decomposition is postponed to the level of the metric entropy or the fat-shattering dimension. On the other hand, this impacts negatively the rate of convergence (dependency on m), an indication of the fact that optimizing the dependencies on the three basic parameters amounts to looking for a trade-off
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Musayeva, Khadija. "Generalization Performance of Margin Multi-category Classifiers." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0096/document.

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Cette thèse porte sur la théorie de la discrimination multi-classe à marge. Elle a pour cadre la théorie statistique de l’apprentissage de Vapnik et Chervonenkis. L’objectif est d’établir des bornes de généralisation possédant une dépendances explicite au nombre C de catégories, à la taille m de l’échantillon et au paramètre de marge gamma, lorsque la fonction de perte considérée est une fonction de perte à marge possédant la propriété d’être lipschitzienne. La borne de généralisation repose sur la performance empirique du classifieur ainsi que sur sa "capacité". Dans cette thèse, les mesures de capacité considérées sont les suivantes : la complexité de Rademacher, les nombres de recouvrement et la dimension fat-shattering. Nos principales contributions sont obtenues sous l’hypothèse que les classes de fonctions composantes calculées par le classifieur ont des dimensions fat-shattering polynomiales et que les fonctions composantes sont indépendantes. Dans le contexte du schéma de calcul introduit par Mendelson, qui repose sur les relations entre les mesures de capacité évoquées plus haut, nous étudions l’impact que la décomposition au niveau de l’une de ces mesures de capacité a sur les dépendances (de la borne de généralisation) à C, m et gamma. En particulier, nous démontrons que la dépendance à C peut être considérablement améliorée par rapport à l’état de l’art si la décomposition est reportée au niveau du nombre de recouvrement ou de la dimension fat-shattering. Ce changement peut affecter négativement le taux de convergence (dépendance à m), ce qui souligne le fait que l’optimisation par rapport aux trois paramètres fondamentaux se traduit par la recherche d’un compromis
This thesis deals with the theory of margin multi-category classification, and is based on the statistical learning theory founded by Vapnik and Chervonenkis. We are interested in deriving generalization bounds with explicit dependencies on the number C of categories, the sample size m and the margin parameter gamma, when the loss function considered is a Lipschitz continuous margin loss function. Generalization bounds rely on the empirical performance of the classifier as well as its "capacity". In this work, the following scale-sensitive capacity measures are considered: the Rademacher complexity, the covering numbers and the fat-shattering dimension. Our main contributions are obtained under the assumption that the classes of component functions implemented by a classifier have polynomially growing fat-shattering dimensions and that the component functions are independent. In the context of the pathway of Mendelson, which relates the Rademacher complexity to the covering numbers and the latter to the fat-shattering dimension, we study the impact that decomposing at the level of one of these capacity measures has on the dependencies on C, m and gamma. In particular, we demonstrate that the dependency on C can be substantially improved over the state of the art if the decomposition is postponed to the level of the metric entropy or the fat-shattering dimension. On the other hand, this impacts negatively the rate of convergence (dependency on m), an indication of the fact that optimizing the dependencies on the three basic parameters amounts to looking for a trade-off
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Zeng, Yue, and Yue Zeng. "Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/624579.

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Variable screening techniques are fast and crude techniques to scan high-dimensional data and conduct dimension reduction before a refined variable selection method is applied. Its marginal analysis feature makes the method computationally feasible for ultra-high dimensional problems. However, most existing screening methods for classification problems are designed only for binary classification problems. There is lack of a comprehensive study on variable screening for multi-class classification problems. This research aims to fill the gap by developing variable screening for multi-class problems, to meet the need of high dimensional classification. The work has useful applications in cancer study, medicine, engineering and biology. In this research, we propose and investigate new and effective screening methods for multi-class classification problems. We consider two types of screening methods. The first one conducts screening for multiple binary classification problems separately and then aggregates the selected variables. The second one conducts screening for multi-class classification problems directly. In particular, for each method we investigate important issues such as choices of classification algorithms, variable ranking, and model size determination. We implement various selection criteria and compare their performance. We conduct extensive simulation studies to evaluate and compare the proposed screening methods with existing ones, which show that the new methods are promising. Furthermore, we apply the proposed methods to four cancer studies. R code has been developed for each method.
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Bonidal, Rémi. "Sélection de modèle par chemin de régularisation pour les machines à vecteurs support à coût quadratique." Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0066/document.

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La sélection de modèle est un thème majeur de l'apprentissage statistique. Dans ce manuscrit, nous introduisons des méthodes de sélection de modèle dédiées à des SVM bi-classes et multi-classes. Ces machines ont pour point commun d'être à coût quadratique, c'est-à-dire que le terme empirique de la fonction objectif de leur problème d'apprentissage est une forme quadratique. Pour les SVM, la sélection de modèle consiste à déterminer la valeur optimale du coefficient de régularisation et à choisir un noyau approprié (ou les valeurs de ses paramètres). Les méthodes que nous proposons combinent des techniques de parcours du chemin de régularisation avec de nouveaux critères de sélection. La thèse s'articule autour de trois contributions principales. La première est une méthode de sélection de modèle par parcours du chemin de régularisation dédiée à la l2-SVM. Nous introduisons à cette occasion de nouvelles approximations de l'erreur en généralisation. Notre deuxième contribution principale est une extension de la première au cas multi-classe, plus précisément à la M-SVM². Cette étude nous a conduits à introduire une nouvelle M-SVM, la M-SVM des moindres carrés. Nous présentons également de nouveaux critères de sélection de modèle pour la M-SVM de Lee, Lin et Wahba à marge dure (et donc la M-SVM²) : un majorant de l'erreur de validation croisée leave-one-out et des approximations de cette erreur. La troisième contribution principale porte sur l'optimisation des valeurs des paramètres du noyau. Notre méthode se fonde sur le principe de maximisation de l'alignement noyau/cible, dans sa version centrée. Elle l'étend à travers l'introduction d'un terme de régularisation. Les évaluations expérimentales de l'ensemble des méthodes développées s'appuient sur des benchmarks fréquemment utilisés dans la littérature, des jeux de données jouet et des jeux de données associés à des problèmes du monde réel
Model selection is of major interest in statistical learning. In this document, we introduce model selection methods for bi-class and multi-class support vector machines. We focus on quadratic loss machines, i.e., machines for which the empirical term of the objective function of the learning problem is a quadratic form. For SVMs, model selection consists in finding the optimal value of the regularization coefficient and choosing an appropriate kernel (or the values of its parameters). The proposed methods use path-following techniques in combination with new model selection criteria. This document is structured around three main contributions. The first one is a method performing model selection through the use of the regularization path for the l2-SVM. In this framework, we introduce new approximations of the generalization error. The second main contribution is the extension of the first one to the multi-category setting, more precisely the M-SVM². This study led us to derive a new M-SVM, the least squares M-SVM. Additionally, we present new model selection criteria for the M-SVM introduced by Lee, Lin and Wahba (and thus the M-SVM²). The third main contribution deals with the optimization of the values of the kernel parameters. Our method makes use of the principle of kernel-target alignment with centered kernels. It extends it through the introduction of a regularization term. Experimental validation of these methods was performed on classical benchmark data, toy data and real-world data
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Bonidal, Rémi. "Sélection de modèle par chemin de régularisation pour les machines à vecteurs support à coût quadratique." Electronic Thesis or Diss., Université de Lorraine, 2013. http://www.theses.fr/2013LORR0066.

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La sélection de modèle est un thème majeur de l'apprentissage statistique. Dans ce manuscrit, nous introduisons des méthodes de sélection de modèle dédiées à des SVM bi-classes et multi-classes. Ces machines ont pour point commun d'être à coût quadratique, c'est-à-dire que le terme empirique de la fonction objectif de leur problème d'apprentissage est une forme quadratique. Pour les SVM, la sélection de modèle consiste à déterminer la valeur optimale du coefficient de régularisation et à choisir un noyau approprié (ou les valeurs de ses paramètres). Les méthodes que nous proposons combinent des techniques de parcours du chemin de régularisation avec de nouveaux critères de sélection. La thèse s'articule autour de trois contributions principales. La première est une méthode de sélection de modèle par parcours du chemin de régularisation dédiée à la l2-SVM. Nous introduisons à cette occasion de nouvelles approximations de l'erreur en généralisation. Notre deuxième contribution principale est une extension de la première au cas multi-classe, plus précisément à la M-SVM². Cette étude nous a conduits à introduire une nouvelle M-SVM, la M-SVM des moindres carrés. Nous présentons également de nouveaux critères de sélection de modèle pour la M-SVM de Lee, Lin et Wahba à marge dure (et donc la M-SVM²) : un majorant de l'erreur de validation croisée leave-one-out et des approximations de cette erreur. La troisième contribution principale porte sur l'optimisation des valeurs des paramètres du noyau. Notre méthode se fonde sur le principe de maximisation de l'alignement noyau/cible, dans sa version centrée. Elle l'étend à travers l'introduction d'un terme de régularisation. Les évaluations expérimentales de l'ensemble des méthodes développées s'appuient sur des benchmarks fréquemment utilisés dans la littérature, des jeux de données jouet et des jeux de données associés à des problèmes du monde réel
Model selection is of major interest in statistical learning. In this document, we introduce model selection methods for bi-class and multi-class support vector machines. We focus on quadratic loss machines, i.e., machines for which the empirical term of the objective function of the learning problem is a quadratic form. For SVMs, model selection consists in finding the optimal value of the regularization coefficient and choosing an appropriate kernel (or the values of its parameters). The proposed methods use path-following techniques in combination with new model selection criteria. This document is structured around three main contributions. The first one is a method performing model selection through the use of the regularization path for the l2-SVM. In this framework, we introduce new approximations of the generalization error. The second main contribution is the extension of the first one to the multi-category setting, more precisely the M-SVM². This study led us to derive a new M-SVM, the least squares M-SVM. Additionally, we present new model selection criteria for the M-SVM introduced by Lee, Lin and Wahba (and thus the M-SVM²). The third main contribution deals with the optimization of the values of the kernel parameters. Our method makes use of the principle of kernel-target alignment with centered kernels. It extends it through the introduction of a regularization term. Experimental validation of these methods was performed on classical benchmark data, toy data and real-world data
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Zimak, Dav Arthur. "Algorithms and analysis for multi-category classification /." 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3223769.

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Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3926. Adviser: Dan Roth. Includes bibliographical references (leaves 114-119) Available on microfilm from Pro Quest Information and Learning.
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Le, Hieu Quang [Verfasser]. "Making use of category structure for multi-class classification / vorgelegt von Hieu Quang Le." 2010. http://d-nb.info/1003341977/34.

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Chiu, Hung-Chih, and 邱泓智. "Construction of Multi-Category Classification Model for Identifying Defective Pixels on TFT-LCD Panels Using Deep Learning Approach." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4v73kv.

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碩士
國立交通大學
工業工程與管理系所
107
Amount and position of defects on TFT-LCD panel affects the level of the panel, adding Automatic optical inspection(AOI) into the light-on test, which can automatically detect defects from the image of LCD panel. This study constructed a model based on Convolutional neural network of deep learning, which can classify various kind of defects at once. Without any pre-processing, the model can automatically capture features through a large number of data and model training, resulting in a high-efficiency and high-accuracy defects classification model. This study used actual panel images from Taiwan's leading computer hardware manufacturers for model construction, model testing and validating the result. After validation, the model constructed by this study has 99.9% model accuracy and excellent specificity and sensitivity, the model can also finish the process of classifying a TFT-LCD panel in only 467 seconds.
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Books on the topic "Multi-category classification"

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Colpan, Asli M., and Takashi Hikino. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198717973.003.0001.

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While business groups are a dominant and critical business organization in contemporary emerging economies and have lately attracted much attention in academic circles and business presses, interestingly their counterparts in developed economies have not been systematically examined. This chapter serves as an introduction to this volume that examines the origins, evolution, and resilience of business groups in the developed economies of Western Europe, North America and Oceania. First, it describes the major aims of the volume and argues why it focuses on business groups in developed economies. Second, it examines the categorical classifications of various types of business groups, as conceptual clarification is necessary to distinguish different varieties of this organizational model at the outset of the volume. Third, the chapter explores the varieties of diversified business groups and their comparable organizational models under the category of “multi-unit enterprises.” It concludes by giving an outline of the entire volume.
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Book chapters on the topic "Multi-category classification"

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Kanaujia, Atul, and Dimitris Metaxas. "Learning Multi-category Classification in Bayesian Framework." In Computer Vision – ACCV 2006, 255–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612032_27.

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Zhang, Congle, and Yong Yu. "Constrained Local Regularized Transducer for Multi-Component Category Classification." In PRICAI 2008: Trends in Artificial Intelligence, 521–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89197-0_48.

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Rastogi, Reshma, and Ritesh Gangnani. "Semi-supervised Multi-category Classification with Generative Adversarial Networks." In Lecture Notes in Computer Science, 286–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34869-4_32.

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Jiang, Jingqing, Chunguo Wu, and Yanchun Liang. "Multi-category Classification by Least Squares Support Vector Regression." In Advances in Neural Networks — ISNN 2005, 863–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427391_138.

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Poulard, H., and N. Hernandez. "A Constructive Algorithm for Real Valued Multi-category Classification Problems." In Artificial Neural Nets and Genetic Algorithms, 527–31. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_116.

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Duan, Kaibo, S. Sathiya Keerthi, Wei Chu, Shirish Krishnaj Shevade, and Aun Neow Poo. "Multi-category Classification by Soft-Max Combination of Binary Classifiers." In Multiple Classifier Systems, 125–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44938-8_13.

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Desyatnikov, Ilya, and Ron Meir. "Data-Dependent Bounds for Multi-category Classification Based on Convex Losses." In Learning Theory and Kernel Machines, 159–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45167-9_13.

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Chong, Chak Fong, Xu Yang, Tenglong Wang, Wei Ke, and Yapeng Wang. "Category-Wise Fine-Tuning for Image Multi-label Classification with Partial Labels." In Communications in Computer and Information Science, 332–45. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8145-8_26.

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Liu, Mark. "Multi-Category Image Classifications." In Machine Learning, Animated, 163–80. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/b23383-9.

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Ghaly, Mohammed. "Constructing a Comprehensive Discourse." In Islamic Ethics and Incidental Findings, 13–23. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59405-2_2.

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AbstractChapter two constructs a comprehensive ethical framework to facilitate the analysis of intricate bioethical issues like incidental findings (IFs) in genomics. Drawing from both secular and Islamic traditions, it synthesizes Robert Veatch's multi-layered approach to bioethics and the recommendation by Muslim ethicists to engage diverse scholarly disciplines. The “Theoretical Level” section explores Islamic metaethics rooted in theology and legal theory, centering on aligning human actions with God’s will to achieve benefit and avert harm. It examines the process of religio-ethical reasoning (ijtihād) employed by Muslim scholars to discern divine guidance on novel issues. The “Practical Level” section outlines the fivefold classification scheme for categorizing human acts based on their moral value within the Islamic tradition: prohibited, obligatory, reprehensible, recommended, and permissible. Distinct from secular schemes, this classification’s theological foundations, definitions, and moral dimensions are elucidated. Bridging theory and practice, the chapter proposes utilizing this fivefold scheme as a nuanced tool to evaluate the ethical management of IFs. It advocates a dynamic approach, acknowledging how evolving scientific understanding may shift the categorization of specific IFs over time. The chapter lays the groundwork for the subsequent analysis, where representative cases illustrating each ethical category are examined through the synthesized Islamic ethical lens, fostering constructive dialogue between religious and secular bioethical discourses on this complex issue.
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Conference papers on the topic "Multi-category classification"

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Rousu, Juho, Craig Saunders, Sandor Szedmak, and John Shawe-Taylor. "Learning hierarchical multi-category text classification models." In the 22nd international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1102351.1102445.

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Saigal, Pooja, and Reshma Khemchandani. "Nonparallel hyperplane classifiers for multi-category classification." In 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI). IEEE, 2015. http://dx.doi.org/10.1109/wci.2015.7495510.

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Wang, Shuo, and Xiaofeng Meng. "Multi-Emotion Category Improving Embedding for Sentiment Classification." In CIKM '18: The 27th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3269206.3269284.

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Zhang, Wei, Liguo Duan, and Junjie Chen. "Study on Chinese question classification based on SVM multi-category classification." In Fourth International Conference on Machine Vision (ICMV 11), edited by Zhu Zeng and Yuting Li. SPIE, 2012. http://dx.doi.org/10.1117/12.923769.

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Helmy, Tarek, and Zeehasham Rasheed. "Multi-category bioinformatics dataset classification using extreme learning machine." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983354.

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Maeda, E., and H. Murase. "Multi-category classification by kernel based nonlinear subspace method." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.759880.

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Yuan, Lutao, Zhenhai Wang, Hui Chen, Hongyu Tian, Ying Ren, Xing Wang, and Peishuai Li. "Multi-Category Fruit Image Classification Based on Interactive Segmentation." In 2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE). IEEE, 2022. http://dx.doi.org/10.1109/ecice55674.2022.10042838.

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Moya, Edison, Emerson Campoverde, Eduardo Tusa, Ivan Ramirez-Morales, Wilmer Rivas, and Bertha Mazon. "Multi-category Classification of Mammograms by Using Convolutional Neural Networks." In 2017 International Conference on Information Systems and Computer Science (INCISCOS). IEEE, 2017. http://dx.doi.org/10.1109/inciscos.2017.56.

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Boukharouba, Khaled, Laurent Bako, and Stéphane Lecoeuche. "Incremental and Decremental Multi-category Classification by Support Vector Machines." In 2009 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2009. http://dx.doi.org/10.1109/icmla.2009.114.

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Kim, Donghyeon, Younglo Lee, and Hanseok Ko. "Multi-task Learning for Animal Species and Group Category Classification." In ICIT 2019: IoT and Smart City. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3377170.3377259.

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Reports on the topic "Multi-category classification"

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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.

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Abstract:
Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
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