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Found 12 papers, 1 papers with code
Random Forest Kernel for High-Dimension Low Sample Size Classification
High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning.
Approximating DTW with a convolutional neural network on EEG data
Dynamic Time Wrapping (DTW) is a widely used algorithm for measuring similarities between two time series.
Pattern Spotting and Image Retrieval in Historical Documents using Deep Hashing
This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents.
scikit-dyn2sel -- A Dynamic Selection Framework for Data Streams
Mining data streams is a challenge per se.
Random Forest for Dissimilarity-based Multi-view Learning
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions.
A Novel Random Forest Dissimilarity Measure for Multi-View Learning
Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task.
Deep Learning Approaches for Image Retrieval and Pattern Spotting in Ancient Documents
This paper describes two approaches for content-based image retrieval and pattern spotting in document images using deep learning.
Image Retrieval and Pattern Spotting using Siamese Neural Network
This paper presents a novel approach for image retrieval and pattern spotting in document image collections.
Pattern Spotting in Historical Documents Using Convolutional Models
Pattern spotting consists of searching in a collection of historical document images for occurrences of a graphical object using an image query.
Dynamic voting in multi-view learning for radiomics applications
Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients.
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images
In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning.
Dissimilarity-based representation for radiomics applications
Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information.
