A review of generalized zero-shot learning methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …
under the condition that some output classes are unknown during supervised learning. To …
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …
considered" de facto" standard in the framework of learning from imbalanced data. This is …
Neighborhood linear discriminant analysis
Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …
are independently and identically distributed (iid). LDA may fail in the cases where the …
Machine learning and deep learning for sentiment analysis across languages: A survey
The inception and rapid growth of the Web, social media, and other online forums have
resulted in the continuous and rapid generation of opinionated textual data. Several real …
resulted in the continuous and rapid generation of opinionated textual data. Several real …
Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models
Since the problem proposed in late 2000s, microRNA–disease association (MDA)
predictions have been implemented based on the data fusion paradigm. Integrating diverse …
predictions have been implemented based on the data fusion paradigm. Integrating diverse …
Word translation without parallel data
A benchmarking study of embedding-based entity alignment for knowledge graphs
Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the
same real-world object. Recent advancement in KG embedding impels the advent of …
same real-world object. Recent advancement in KG embedding impels the advent of …
K-means properties on six clustering benchmark datasets
This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …
we study the performance of k-means using this benchmark. Specifically, we measure how …
Semantic autoencoder for zero-shot learning
Existing zero-shot learning (ZSL) models typically learn a projection function from a feature
space to a semantic embedding space (eg attribute space). However, such a projection …
space to a semantic embedding space (eg attribute space). However, such a projection …