Remote sensing image segmentation by active queries
Graph Chatbot
Chattez avec Graph Search
Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...
Structural identification using physics-based models and subsequent prediction have much potential to enhance civil infrastructure asset-management decision-making. Interpreting monitoring information in the presence of multiple uncertainty sources and sys ...
Modeling and predicting student learning in computer-based environments often relies solely on sequences of accuracy data. Previous research suggests that it does not only matter what we learn, but also how we learn. The detection and analysis of learning ...
We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real-time at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class ...
The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective ...
Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice ...
Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...
This article presents two algorithms developed based on two different techniques, from clusterization theory, namely k-means clustering technique and Fuzzy C-means technique, respectively. In this context, the study offers a sustained comparison of the two ...
We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generali ...
Clustering is a method for discovering structure in data, widely used across many scientific disciplines. The two main clustering problems this dissertation considers are K-means and K-medoids. These are NP-hard problems in the number of samples and cluste ...