arXiv:2606. 17858v1 Announce Type: new Abstract: Machine Learning (ML) techniques have been applied to various problems.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 16002v1 Announce Type: new Abstract: One-class classification (OCC) is a classification problem in which the training data contains only one class.
arXiv:2607. 01297v1 Announce Type: cross Abstract: Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of unseen classes).
The paper introduces a method for combining heterogeneous, allied datasets—datasets that share the same class labels but have disjoint objects and largely distinct feature spaces—into a single unified feature space. By applying matrix completion to this merged space, the authors create a unified dataset that enables knowledge transfer between the original datasets. Experiments across multiple dataset pairs and classifiers show that models trained on the unified representation consistently outperform those trained separately on each dataset.
arXiv:2101. 05993v2 Announce Type: replace-cross Abstract: Selecting an appropriate classification algorithm for a given data set remains a challenging problem in data mining and machine learning.
arXiv:2607. 21003v1 Announce Type: new Abstract: Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order.
The paper introduces a framework that learns the kernel used in kernel methods through alignment, leveraging the Collaborative Learning and Inference (CLaI) approach. It demonstrates that CLaI can be interpreted as a kernel alignment process and that its inference stage is equivalent to kernel Bayes classification with Parzen-window density estimation. By replacing cosine similarity with a learned Mahalanobis distance, the authors extend CLaI to multiclass classification, achieving higher accuracy, faster convergence, and lower calibration error on datasets such as CIFAR-10, PathMNIST, and SleepEDF, while also showing connections to Gaussian processes and competitive calibration in sepsis prediction.