Relevant bibliographies by topics / Multiclass classifications

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  1. Dissertations / Theses

Academic literature on the topic 'Multiclass classifications'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Dissertations / Theses on the topic "Multiclass classifications"

1

Silva, Palacios Daniel Andrés. "Clasificación Jerárquica Multiclase." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/167015.

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[ES] La sociedad moderna se ha visto afectada por los acelerados avances de la tecnología. La aplicación de la inteligencia artificial se puede encontrar en todas partes, desde la televisión inteligente hasta los coches autónomos. Una tarea esencial del aprendizaje automático es la clasificación. A pesar de la cantidad de técnicas y algoritmos de clasificación que existen, es un campo que sigue siendo relevante por todas sus aplicaciones. Así, frente a la clasificación tradicional multiclase en la que a cada instancia se le asigna una única etiqueta de clase, se han propuesto otros métodos com
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Abouelenien, Mohamed. "Boosting for Learning From Imbalanced, Multiclass Data Sets." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc407775/.

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In many real-world applications, it is common to have uneven number of examples among multiple classes. The data imbalance, however, usually complicates the learning process, especially for the minority classes, and results in deteriorated performance. Boosting methods were proposed to handle the imbalance problem. These methods need elongated training time and require diversity among the classifiers of the ensemble to achieve improved performance. Additionally, extending the boosting method to handle multi-class data sets is not straightforward. Examples of applications that suffer from imbal
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Huang, Jian Giles C. Lee. "A multiclass boosting classification method with active learning." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4765/index.html.

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Grünwald, Adam. "APPLICATIONS OF DEEP LEARNING IN TEXT CLASSIFICATION FOR HIGHLY MULTICLASS DATA." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385162.

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Text classification using deep learning is rarely applied to tasks with more than ten target classes. This thesis investigates if deep learning can be successfully applied to a task with over 1000 target classes. A pretrained Long Short-Term Memory language model is fine-tuned and used as a base for the classifier. After five days of training, the deep learning model achieves 80.5% accuracy on a publicly available dataset, 9.3% higher than Naive Bayes. With five guesses, the model predicts the correct class 92.2% of the time.
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Ringdahl, Benjamin. "Gaussian Process Multiclass Classification : Evaluation of Binarization Techniques and Likelihood Functions." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87952.

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In binary Gaussian process classification the prior class membership probabilities are obtained by transforming a Gaussian process to the unit interval, typically either with the logistic likelihood function or the cumulative Gaussian likelihood function. Multiclass classification problems can be handled by any binary classifier by means of so-called binarization techniques, which reduces the multiclass problem into a number of binary problems. Other than introducing the mathematics behind the theory and methods behind Gaussian process classification, we compare the binarization techniques one
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Park, Sang-Hyeun [Verfasser], Johannes [Akademischer Betreuer] Fürnkranz, and Eyke [Akademischer Betreuer] Hüllermeier. "Efficient Decomposition-Based Multiclass and Multilabel Classification / Sang-Hyeun Park. Betreuer: Johannes Fürnkranz ; Eyke Hüllermeier." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2012. http://d-nb.info/1106115678/34.

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Roxbergh, Linus. "Language Classification of Music Using Metadata." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-379625.

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The purpose of this study was to investigate how metadata from Spotify could be used to identify the language of songs in a dataset containing nine languages. Features based on song name, album name, genre, regional popularity and vectors describing songs, playlists and users were analysed individually and in combination with each other in different classifiers. In addition to this, this report explored how different levels of prediction confidence affects performance and how it compared to a classifier based on audio input. A random forest classifier proved to have the best performance with a
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Sarika, Pawan Kumar. "Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213.

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Today, we are living in a data-driven world. Due to a surge in data generation, there is a need for efficient and accurate techniques to analyze data. One such kind of data which is needed to be analyzed are text reviews given for movies. Rather than classifying the reviews as positive or negative, we will classify the sentiment of the reviews on the scale of one to ten. In doing so, we will compare two recurrent neural network algorithms Long short term memory(LSTM) and Gated recurrent unit(GRU). The main objective of this study is to compare the accuracies of LSTM and GRU models. For trainin
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Mathieu, Bérangère. "Segmentation interactive multiclasse d'images par classification de superpixels et optimisation dans un graphe de facteurs." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30290/document.

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La segmentation est l'un des principaux thèmes du domaine de l'analyse d'images. Segmenter une image consiste à trouver une partition constituée de régions, c'est-à-dire d'ensembles de pixels connexes homogènes selon un critère choisi. L'objectif de la segmentation consiste à obtenir des régions correspondant aux objets ou aux parties des objets qui sont présents dans l'image et dont la nature dépend de l'application visée. Même s'il peut être très fastidieux, un tel découpage de l'image peut être facilement obtenu par un être humain. Il n'en est pas de même quand il s'agit de créer un program
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Bourel, Mathias. "Agrégation de modèles en apprentissage statistique pour l'estimation de la densité et la classification multiclasse." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4076/document.

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Les méthodes d'agrégation en apprentissage statistique combinent plusieurs prédicteurs intermédiaires construits à partir du même jeu de données dans le but d'obtenir un prédicteur plus stable avec une meilleure performance. Celles-ci ont été amplement étudiées et ont données lieu à plusieurs travaux, théoriques et empiriques dans plusieurs contextes, supervisés et non supervisés. Dans ce travail nous nous intéressons dans un premier temps à l'apport de ces méthodes au problème de l'estimation de la densité. Nous proposons plusieurs estimateurs simples obtenus comme combinaisons linéaires d'hi
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