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Дисертації з теми "Incremental neural network"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 27 липня 2025

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1

Lundberg, Emil. "Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180346.

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Анотація:
Vector Quantization (VQ) is a classic optimization problem and a simple approach to pattern recognition. Applications include lossy data compression, clustering and speech and speaker recognition. Although VQ has largely been replaced by time-aware techniques like Hidden Markov Models (HMMs) and Dynamic Time Warping (DTW) in some applications, such as speech and speaker recognition, VQ still retains some significance due to its much lower computational cost — especially for embedded systems. A recent study also demonstrates a multi-section VQ system which achieves performance rivaling that of
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2

Flores, João Henrique Ferreira. "ARMA-CIGMN : an Incremental Gaussian Mixture Network for time series analysis and forecasting." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/116126.

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Анотація:
Este trabalho apresenta um novo modelo de redes neurais para análise e previsão de séries temporais: o modelo ARMA-CIGMN (do inglês, Autoregressive Moving Average Classical Incremental Gaussian Mixture Network) além dos resultados obtidos pelo mesmo. Este modelo se baseia em modificações realizadas em uma versão reformulada da IGMN. A IGMN Clássica, CIGMN, é similar à versão original da IGMN, porém baseada em uma abordagem estatística clássica, a qual também é apresentada neste trabalho. As modificações do algoritmo da IGMN foram feitas para melhor adpatação a séries temporais. O modelo ARMA-C
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3

Rouzier, Sophie. "Réseaux neuronaux et modularité." Grenoble INPG, 1998. http://www.theses.fr/1998INPG0032.

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Анотація:
Face a un probleme complexe, deux types de strategies peuvent etre envisagees : - la premiere consiste a utiliser differentes methodes pour resoudre le probleme global, - la deuxieme consiste a faire cooperer plusieurs methodes specialisees sur differents sous-problemes. Une architecture neuronale adoptant l'une ou l'autre de ces strategies fait preuve de modularite dans la mesure ou d'une part chaque module represente une methode, et d'autre part le traitement de l'ensemble resulte de la cooperation de l'ensemble des modules. Un des buts de ces architectures est l'amelioration des performance
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4

Hocquet, Guillaume. "Class Incremental Continual Learning in Deep Neural Networks." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST070.

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Анотація:
Nous nous intéressons au problème de l'apprentissage continu de réseaux de neurones artificiels dans le cas où les données ne sont accessibles que pour une seule catégorie à la fois. Pour remédier au problème de l'oubli catastrophique qui limite les performances d'apprentissage dans ces conditions, nous proposons une approche basée sur la représentation des données d'une catégorie par une loi normale. Les transformations associées à ces représentations sont effectuées à l'aide de réseaux inversibles, qui peuvent alors être entraînés avec les données d'une seule catégorie. Chaque catégorie se v
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5

Buttar, Sarpreet Singh. "Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87117.

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Анотація:
Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation
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6

Ronco, Eric. "Incremental polynomial controller networks two self-organising non-linear controllers /." Thesis, Connect to electronic version, 1997. http://hdl.handle.net/1905/181.

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7

Monica, Riccardo. "Deep Incremental Learning for Object Recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12331/.

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Анотація:
In recent years, deep learning techniques received great attention in the field of information technology. These techniques proved to be particularly useful and effective in domains like natural language processing, speech recognition and computer vision. In several real world applications deep learning approaches improved the state-of-the-art. In the field of machine learning, deep learning was a real revolution and a number of effective techniques have been proposed for both supervised and unsupervised learning and for representation learning. This thesis focuses on deep learning for object
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8

Pinto, Rafael Coimbra. "Continuous reinforcement learning with incremental Gaussian mixture models." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/157591.

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Анотація:
A contribução original desta tese é um novo algoritmo que integra um aproximador de funções com alta eficiência amostral com aprendizagem por reforço em espaços de estados contínuos. A pesquisa completa inclui o desenvolvimento de um algoritmo online e incremental capaz de aprender por meio de uma única passada sobre os dados. Este algoritmo, chamado de Fast Incremental Gaussian Mixture Network (FIGMN) foi empregado como um aproximador de funções eficiente para o espaço de estados de tarefas contínuas de aprendizagem por reforço, que, combinado com Q-learning linear, resulta em performance com
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9

Pinto, Rafael Coimbra. "Online incremental one-shot learning of temporal sequences." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/49063.

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Анотація:
Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo IGMN é uma rede neural online incremental que aprende a partir de uma única passada através de dados por meio de uma versão incremental do algoritmo Expectation-Maximization (EM) combinado com regressão localmente ponderada (Locally Weighted Regression, LWR). Quatro abordagens diferentes são usadas para dar capacidade de processamento temporal para o algoritmo IGMN: linhas de atraso (Time-Delay IGMN), u
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10

Chalup, Stephan Konrad. "Incremental learning with neural networks, evolutionary computation and reinforcement learning algorithms." Thesis, Queensland University of Technology, 2001.

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11

Glöde, Isabella. "Autonomous control of a mobile robot with incremental deep learning neural networks." Master's thesis, Pontificia Universidad Católica del Perú, 2021. http://hdl.handle.net/20.500.12404/18676.

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Анотація:
Over the last few years autonomous driving had an increasingly strong impact on the automotive industry. This created an increased need for artificial intelligence algo- rithms which allow for computers to make human-like decisions. However, a compro- mise between the computational power drawn by these algorithms and their subsequent performance must be found to fulfil production requirements. In this thesis incremental deep learning strategies are used for the control of a mobile robot such as a four wheel steering vehicle. This strategy is similar to the human approach of learning. In many
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12

Thuv, Øyvin Halfdan. "Incrementally Evolving a Dynamic Neural Network for Tactile-Olfactory Insect Navigation." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8834.

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Анотація:
<p>This Masters thesis gives a thorough description of a study carried out in the Self-Organizing Systems group at the NTNU. Much {AI research in the later years has moved towards increased use of representationless strategies such as simulated neural networks. One technique for creating such networks is to evolve them using simulated Darwinian evolution. This is a powerful technique, but it is often limited by the computer resources available. One way to speed up evolution, is to focus the evolutionary search on a more narrow range of solutions. It is for example possible to favor evolution
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13

Johansson, Philip. "Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158225.

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Анотація:
Medical doctors understaffing is becoming a compelling problem in many healthcare systems. This problem can be alleviated by utilising Computer-Aided Diagnosis (CAD) systems to substitute doctors in different tasks, for instance, histopa-thological image classification. The recent surge of deep learning has allowed CAD systems to perform this task at a very competitive performance. However, a major challenge with this task is the need to periodically update the models with new data and/or new classes or diseases. These periodical updates will result in catastrophic forgetting, as Convolutional
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14

Soares, Sérgio Aurélio Ferreira. "Spatial interpolation and geostatistic simulation with the incremental Gaussian mixture network." reponame:Repositório Institucional da UFSC, 2016. https://repositorio.ufsc.br/xmlui/handle/123456789/178581.

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Анотація:
Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2016.<br>Made available in DSpace on 2017-08-22T04:22:16Z (GMT). No. of bitstreams: 1 347911.pdf: 1690914 bytes, checksum: e43f9150ef3cb130f6d5696b46a68fa5 (MD5) Previous issue date: 2016<br>Abstract : Geostatistics aggregates a set of tools designed to deal with spatially correlated data. Two significant problems that Geostatistics tackles are the spatial interpolation and geostatistical simulation. Kriging and Sequential Gaussian Simulatio
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15

Heinen, Milton Roberto. "A connectionist approach for incremental function approximation and on-line tasks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/29015.

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Анотація:
Este trabalho propõe uma nova abordagem conexionista, chamada de IGMN (do inglês Incremental Gaussian Mixture Network), para aproximação incremental de funções e tarefas de tempo real. Ela é inspirada em recentes teorias do cérebro, especialmente o MPF (do inglês Memory-Prediction Framework) e a Inteligência Artificial Construtivista, que fazem com que o modelo proposto possua características especiais que não estão presentes na maioria dos modelos de redes neurais existentes. Além disso, IGMN é baseado em sólidos princípios estatísticos (modelos de mistura gaussianos) e assintoticamente conve
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16

Pereira, Renato de Pontes. "HIGMN : an IGMN-based hierarchical architecture and its applications for robotic tasks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/80752.

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Анотація:
O recente campo de Deep Learning introduziu a área de Aprendizagem de Máquina novos métodos baseados em representações distribuídas e abstratas dos dados de treinamento ao longo de estruturas hierárquicas. A organização hierárquica de camadas permite que esses métodos guardem informações distribuídas sobre os sinais sensoriais e criem conceitos com diferentes níveis de abstração para representar os dados de entrada. Este trabalho investiga o impacto de uma estrutura hierárquica inspirada pelas ideias apresentadas em Deep Learning, e com base na Incremental Gaussian Mixture Network (IGMN), uma
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17

Cholet, Stéphane. "Evaluation automatique des états émotionnels et dépressifs : vers un système de prévention des risques psychosociaux." Thesis, Antilles, 2019. http://www.theses.fr/2019ANTI0388/document.

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Анотація:
Les risques psychosociaux sont un enjeu de santé publique majeur, en particulier à cause des troubles qu'ils peuvent engendrer : stress, changements d'humeurs, burn-out, etc. Bien que le diagnostic de ces troubles doive être réalisé par un professionel, l'Affective Computing peut apporter une contribution en améliorant la compréhension des phénomènes. L'Affective Computing (ou Informatique Affective) est un domaine pluridisciplinaire, faisant intervenir des concepts d'Intelligence Artificielle, de psychologie et de psychiatrie, notamment. Dans ce travail de recherche, on s'intéresse à deux élé
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18

Börthas, Lovisa, and Sjölander Jessica Krange. "Machine Learning Based Prediction and Classification for Uplift Modeling." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266379.

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Анотація:
The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investiga
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19

Hamid, Muhammed Hamed. "Hyperspectral Image Generation, Processing and Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5905.

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20

Osório, Fernando Santos. "Inss : un système hybride neuro-symbolique pour l'apprentissage automatique constructif." Grenoble INPG, 1998. https://tel.archives-ouvertes.fr/tel-00004899.

Повний текст джерела
Анотація:
Plusieurs méthodes ont été développées par l'Intelligence Artificielle pour reproduire certains aspects de l'intelligence humaine. Ces méthodes permettent de simuler les processus de raisonnement en s'appuyant sur les connaissances de base disponibles. Chaque méthode comporte des points forts, mais aussi des limitations. La réalisation de systèmes hybrides est une démarche courante Qui permet de combiner les points forts de chaque approche, et d'obtenir ainsi des performances plus élevées ou un champ d'application plus large. Un autre aspect très important du développement des systèmes hybride
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21

Besedin, Andrey. "Continual forgetting-free deep learning from high-dimensional data streams." Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1263.

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Анотація:
Dans cette thèse, nous proposons une nouvelle approche de l’apprentissage profond pour la classification des flux de données de grande dimension. Au cours des dernières années, les réseaux de neurones sont devenus la référence dans diverses applications d’apprentissage automatique. Cependant, la plupart des méthodes basées sur les réseaux de neurones sont conçues pour résoudre des problèmes d’apprentissage statique. Effectuer un apprentissage profond en ligne est une tâche difficile. La principale difficulté est que les classificateurs basés sur les réseaux de neurones reposent généralement su
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22

Daou, Andrea. "Real-time Indoor Localization with Embedded Computer Vision and Deep Learning." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR002.

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Анотація:
La localisation d'une personne ou d'un bien dans des environnements intérieurs est devenue une nécessité. Le système de positionnement par satellites, une solution prédominante pour la localisation en extérieur, rencontre des limites lorsqu'il est appliqué en intérieur en raison de la réflexion des signaux et de l'atténuation causée par les obstacles. Pour y remédier, diverses solutions de localisation en intérieur ont été étudiées. Les méthodes de localisation en intérieur sans fil exploitent les signaux pour déterminer la position d'un appareil dans un environnement intérieur. Cependant, l'i
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23

Jiech-Chyn, Wu, and 吳戒秦. "Surface Data Compression by Incremental Approximation of RBF Neural Network." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/85819174739885685144.

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Анотація:
碩士<br>國立臺灣大學<br>電機工程研究所<br>84<br>The measurement of three-dimension surface by scanning usually generates huge amount of data occupying very large space of storage. In stead of saving the dimensional data directly, this paper proposes to approximate the surface by learning the measured data with a neural network and then simply saves the weights of the neural network in the storage. This approach is found to be able to achieve simultaneously the results of approximation, smoothing and, more
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24

Іванова, Є. В. "Самоорганізовна інкрементна нейронна мережа для кластерування масивів даних". Thesis, 2018. https://openarchive.nure.ua/handle/document/20022.

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Анотація:
In the paper self-organizing incremental neural network for multidimensional data sets. It is proposed to accomplish the task of clustering and represent the topological structure of the input data. Self-organizing incremental neural network find typical prototypes for large-scale data set and robust to noise.
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25

Van, der Stockt Stefan Aloysius Gert. "A generic neural network framework using design patterns." Diss., 2008. http://hdl.handle.net/2263/27614.

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Анотація:
Designing object-oriented software is hard, and designing reusable object-oriented software is even harder. This task is even more daunting for a developer of computational intelligence applications, as optimising one design objective tends to make others inefficient or even impossible. Classic examples in computer science include ‘storage vs. time’ and ‘simplicity vs. flexibility.’ Neural network requirements are by their very nature very tightly coupled – a required design change in one area of an existing application tends to have severe effects in other areas, making the change impossible
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26

De, Wet Anton Petrus Christiaan. "An incremental learning system for artificial neural networks." Thesis, 2014. http://hdl.handle.net/10210/12024.

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Анотація:
M.Ing. (Electrical And Electronic Engineering)<br>This dissertation describes the development of a system of Artificial Neural Networks that enables the incremental training of feed forward neural networks using supervised training algorithms such as back propagation. It is argued that incremental learning is fundamental to the adaptive learning behavior observed in human intelligence and constitutes an imperative step towards artificial cognition. The importance of developing incremental learning as a system of ANNs is stressed before the complete system is presented. Details of the developme
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27

Wu, Yi-Lian, and 吳易璉. "Integrated the Validation Incremental Neural Networks and Radial-Basis Function Neural Networks for Segmenting Prostate." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/92540595174319775086.

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Анотація:
碩士<br>國立雲林科技大學<br>資訊工程研究所<br>97<br>Recently, Transrectal ultrasoundgraphy (TRUS) imaging is widely used to diagnose prostate disease. Before a physician can diagnose prostate lesions, contour of the prostate in TRUS images must be manually outlined. However, manual segmentation is time-consuming and inefficient. Therefore, an automatic segmentation of prostate in TRUS images is necessary. Among the segmentation methods, active contour model (ACM) is a successful contour detection method. But the shortcoming of ACM is that the determination of the initial contour is manual. Thus, in this paper,
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28

Obenauff, Alexander. "A progressive learning method for classification of manufacturing errors based on machine data." Master's thesis, 2019. http://hdl.handle.net/10362/76579.

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Анотація:
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Manufacturing companies face significant market pressure in today’s globalised world. Fierce global competition and product individualisation mean that production systems require continuous optimisation. This means that automation, flexibility and efficiency have all become vital elements for manufacturers. In this paper, a method based on incremental classification used for manufacturing errors is presented. The analysis and classification focus on data of binary form c
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29

"Incremental Learning With Sample Generation From Pretrained Networks." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.57207.

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Анотація:
abstract: In the last decade deep learning based models have revolutionized machine learning and computer vision applications. However, these models are data-hungry and training them is a time-consuming process. In addition, when deep neural networks are updated to augment their prediction space with new data, they run into the problem of catastrophic forgetting, where the model forgets previously learned knowledge as it overfits to the newly available data. Incremental learning algorithms enable deep neural networks to prevent catastrophic forgetting by retaining knowledge of previously obser
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30

HUANG, HONG-WEI, and 黃弘偉. "Abnormal Moving Object Detection under Various Enviroments Using Self-Organizing Incremental Neural Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/c54ss4.

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Анотація:
碩士<br>國立臺灣科技大學<br>資訊工程系<br>100<br>Abnormal moving objects detection is an essential issue for video surveillance. In order to judge whether the behavior of objects is abnormal, such as pedestrians walk back and forth, walk across the street, or scooters drive the wrong way, the main method is through computer vision technique to analyze objects as pedestrians, cars, and so on in video. Traditional abnormal moving objects detection aims at particular circumstances or requirement to predefine particular detection rules which the application of abnormal moving objects detection is restricted. Bes
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31

Mohamed, Shakir. "Dynamic protein classification: Adaptive models based on incremental learning strategies." Thesis, 2008. http://hdl.handle.net/10539/4678.

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Анотація:
Abstract One of the major problems in computational biology is the inability of existing classification models to incorporate expanding and new domain knowledge. This problem of static classification models is addressed in this thesis by the introduction of incremental learning for problems in bioinformatics. The tools which have been developed are applied to the problem of classifying proteins into a number of primary and putative families. The importance of this type of classification is of particular relevance due to its role in drug discovery programs and the benefit it lends to thi
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32

Del, Chiaro Riccardo. "Anthropomorphous Visual Recognition: Learning with Weak Supervision, with Scarce Data, and Incrementally over Transient Tasks." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238101.

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Анотація:
In the last eight years the computer vision field has experienced dramatic improvements thanks to the widespread availability of data and affordable parallel computing hardware like GPUs. These two factors have contributed to making possible the training of very deep neural network models in reasonable times using millions of labeled examples for supervision. Humans do not learn concepts in this way. We do not need a massive number of labeled examples to learn new concepts; instead we rely on a few (or even zero) examples, infer missing information, and generalize. Moreover, we retain previous
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33

Chang, Da-Yuan, and 張大元. "A Study on Water Stage Increment of the River due to Reservoir Drainage by Artificial Neural Network." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/20190379759396040336.

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Анотація:
碩士<br>中原大學<br>土木工程研究所<br>92<br>Abstract In Taiwan, roughly 78% of its yearly rainfall concentrates in the summer and autumn because of the particular climate and geographic characteristics. During Typhoon period, the reservoir operators often face the dilemma of maintaining more floodwater and taking the risk of failure of the dam and taking the risk of being drought if excess floodwater is released. The most difficult task of reservoir operation is to consider all the functions of the reservoir. To achieve this purpose, forecasting the inflows of reservoir and simulating downstream water-sta
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34

Santos, Veríssimo Manuel Brandão Lima. "Deteção automática de lesões no intestino delgado por análise de imagens obtidas por cápsula endoscópica." Doctoral thesis, 2020. http://hdl.handle.net/1822/76137.

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Анотація:
Tese de Doutoramento em Engenharia Biomédica<br>Nas últimas duas décadas foram propostas numerosas metodologias de deteção automática de lesões por análise de imagens obtidas por cápsula endoscópica, com vista à automatização do moroso processo de revisão das imagens, utilizando uma grande variedade de pré-processamentos e classificadores. Um contributo significativo para aumentar a eficácia na deteção de lesões poderá ser obtido pelo uso de classificadores de elevado desempenho. Os conjuntos de dados obtidos em endoscopia por cápsula frequentemente apresentam distribuições multimodais, que
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