Most of the semi-supervised classification methods developed so far use unlabeled data for
regularization purposes under particular distributional assumptions such as the cluster …

This paper proposes a fast spectral clustering method for large-scale data. In the present
method, random projection and random sampling techniques are adopted for reducing the data …

Overparameterized deep networks have the capacity to memorize training data with zero \emph{training
error}. Even after memorization, the \emph{training loss} continues to approach …

In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without
negative (N) data. Although N data is missing, it sometimes outperforms PN learning (ie, …

The goal of* generalized* few-shot semantic segmentation (GFSS) is to recognize* novel-class*
objects through training with a few annotated examples and the* base-class* model that …

Maximizing the area under the receiver operating characteristic curve (AUC) is a standard
approach to imbalanced classification. So far, various supervised AUC optimization methods …

… In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang
Niu present theory and algorithms for weakly supervised learning, a paradigm of machine …

The goal of binary classification is to identify whether an input sample belongs to positive or
negative classes. Usually, supervised learning is applied to obtain a classification rule, but …

Multiple instance learning (MIL) is a variation of traditional supervised learning problems
where data (referred to as bags) are composed of sub-elements (referred to as instances) and …

This paper presents results of our study on double-precision general matrix-matrix multiplication
(DGEMM) for GPU-equipped systems. We applied further optimization to utilize the …