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Listar por palabra clave "Unsupervised learning"

Mostrando ítems 1-18 de 18

A novel self-learning framework for bladder cancer grading using histopathological images

García-Pardo, José Gabriel; Esteve, Anna; Colomer, Adrián; Ramos, David; Naranjo Ornedo, Valeriana (Elsevier, 2021-11)

[EN] In recent times, bladder cancer has increased significantly in terms of incidence and mortality. Currently, two subtypes are known based on tumour growth: non-muscle invasive (NMIBC) and muscle-invasive bladder cancer ...
Aplicación de Técnicas de Aprendizaje Automático Orientadas al Diagnóstico Médico

Nocedal González, Pablo (Universitat Politècnica de València, 2020-01-02)

[ES] Cada vez más se generan grandes volúmenes de datos de los que es posible extraer información de gran relevancia, y el área de la salud no es una excepción en este sentido. Estos datos permiten emplear técnicas de ...
Archetypoids: A new approach to define representative archetypal data

Vinue, G.; Epifanio, I.; Alemany Mut, Mª Sandra (Elsevier, 2015)

[EN] The new concept archetypoids is introduced. Archetypoid analysis represents each observation in a dataset as a mixture of actual observations in the dataset, which are pure type or archetypoids. Unlike archetype ...
Clustering: C-means algorithm

Juan Císcar, Alfonso; Sanchis Navarro, José Alberto; Civera Saiz, Jorge (Universitat Politècnica de València, 2019-05-23)

The training objectives of the learning object are: 1) To analyze the partitional clustering problem under the Sum of Squared Errors criterion; 2) Learning object to introduce clustering and the C-means (or K-means) algorithm ...
Comparative implementation of Greedy InfoMax and Slow Feature Analysis for self-supervised neural networks

Roselló Oviedo, Manuel (Universitat Politècnica de València, 2020-11-16)

[EN] Over the past years, supervised models with end-to-end backpropagation have dominated the Deep Learning paradigm. However, great potential can be observed in out-of-the-box approaches like Greedy InfoMax, an ...
Descubrimiento de grupos a partir de datos no supervisados mediante algoritmos de clustering

Baeza García, Jorge (Universitat Politècnica de València, 2024-09-09)

[ES] Fermax, una empresa innovadora en el sector de la tecnología, enfrenta el desafío de gestionar y analizar grandes volúmenes de datos generados por sus productos. El presente estudio se centra en descubrir patrones ...
Efficient training of energy-based models via spin-glass control

Pozas-Kerstjens, Alejandro; Muñoz-Gil, Gorka; Piñol, Eloy; Garcia March, Miguel Angel; Acín, Antonio; Lewenstein, Maciej; Grzybowski, Przemyslaw R. (IOP Publishing, 2021-06)

[EN] We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning. Its definition is motivated by the control of the spin-glass properties of the Ising model described by the ...
Image-to-image domain translation using CycleGAN

Furió Agustí, Juan Alejandro (Universitat Politècnica de València, 2022-10-19)

[CA] Els últims avanços en el camp de les xarxes neuronals han portat al’auge de les xarxes generatives, les quals necessiten grans quantitats de dades per a entrenar-se. Aquestes dades moltes vegades existeixen en formats ...
Mejorando la interpretación de series temporales usando aprendizaje no supervisado

Sánchez, Jaime (Editorial Universitat Politècnica de València, 2021-10-01)

[EN] DInSAR technology provides high density of information about the temporal ground and infrastructure deformation rate with millimetre precision and without the need of ground instrumentation. DInSAR analyses the ...
Muscle fatigue analysis during dynamic contractions based on biomechanical features and Permutation Entropy

Murillo-Escobar, J.; Jaramillo Munera, Y. E.; Orrego-Metaute, D. A.; Delgado-Trejos, E.; Cuesta Frau, David (2020)

[EN] Muscle fatigue is an important field of study in sports medicine and occupational health. Several studies in the literature have proposed methods for predicting muscle fatigue in isometric contractions using three ...
Non-local spatially varying finite mixture models for image segmentation

Juan -Albarracín, Javier; Fuster García, Elíes; Juan, Alfons; Garcia-Gomez, Juan M (Springer-Verlag, 2021-01-12)

[EN] In this work, we propose a new Bayesian model for unsupervised image segmentation based on a combination of the spatially varying finite mixture models (SVFMMs) and the non-local means (NLM) framework. The probabilistic ...
ONCOhabitats: A system for glioblastoma heterogeneity assessment through MRI

Juan -Albarracín, Javier; Fuster García, Elíes; García-Ferrando, Germán Adrián; Garcia-Gomez, Juan M (Elsevier, 2019-08)

[EN] Background: Neuroimaging analysis is currently crucial for an early assessment of glioblastoma, to help improving treatment and tumor follow-up. To this end, multiple functional and morphological MRI sequences are ...
Reconocimiento Multimodal Automatizado de Emociones

Fernández Carbonell, Marcos (Universitat Politècnica de València, 2020-10-19)

[ES] Ser capaz de leer e interpretar estados afectivos juega un papel importante en la sociedad. Sin embargo, esto es difícil en algunas situaciones, especialmente cuando la información se limita a señales vocales o visuales. ...
RT-MOVICAB-IDS: Addressing Real-Time Intrusion Detection

Herrero, Álvaro; Navarro Llácer, Martín; Corchado, Emilio; Julian Inglada, Vicente Javier (Elsevier, 2013-01)

[EN] This study presents a novel Hybrid Intelligent Intrusion Detection System (IDS) known as RT-MOVICABIDS that incorporates temporal control. One of its main goals is to facilitate real-time Intrusion Detection, as ...
Toward More Transparent and Accurate Cancer Diagnosis With an Unsupervised CAE Approach

Tabatabaei, Zahra; Colomer, Adrián; Oliver Moll, Javier; Naranjo Ornedo, Valeriana (Institute of Electrical and Electronics Engineers, 2023-12-18)

[EN] According to the Global Cancer Observatory, 2020, breast cancer is the most prevalent cancer type in both genders (11.7%), while prostate cancer is the second most common cancer type in men (14.1%). In digital pathology, ...
Unsupervised Learning for Lateral-Movement-Based Threat Mitigation in Active Directory Attack Graphs

Herranz-Oliveros, David; Tejedor-Romero, Marino; Gimenez-Guzman, Jose Manuel; de la Cruz-Piris, Luis (MDPI AG, 2024-10)

[EN] Cybersecurity threats, particularly those involving lateral movement within networks, pose significant risks to critical infrastructures such as Microsoft Active Directory. This study addresses the need for effective ...
Unsupervised Learning for the Analysis and Detection of Fraud in the Insurance Industry

Alvarez-Jareño, José A.; Pavía, José Manuel (Editorial Universitat Politècnica de València, 2022-09-20)

[EN] Analysis and detection of fraud in the insurance sector has traditionally been carried out through supervised learning. The main problem is that the data presents a strong imbalance and techniques are used to balance ...
Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature

Juan Albarracín, Javier (Universitat Politècnica de València, 2020-09-02)

[ES] El futuro de la imagen médica está ligado a la inteligencia artificial. El análisis manual de imágenes médicas es hoy en día una tarea ardua, propensa a errores y a menudo inasequible para los humanos, que ha llamado ...