Multi-Instance Nonparallel Tube Learning.

In multi-instance nonparallel plane learning (NPL), the training set is comprised of bags of instances and the nonparallel planes are trained to classify the bags. Most of the existing multi-instance NPL methods are proposed based on a twin support vector machine (TWSVM). Similar to TWSVM, they use only a single plane to generalize the data occurrence of one class and do not sufficiently consider the boundary information, which may lead to the limitation of their classification accuracy.

Ordinal Regression With Pinball Loss.

Ordinal regression (OR) aims to solve multiclass classification problems with ordinal classes. Support vector OR (SVOR) is a typical OR algorithm and has been extensively used in OR problems. In this article, based on the characteristics of OR problems, we propose a novel pinball loss function and present an SVOR method with pinball loss (pin-SVOR).

A Multitask Latent Feature Augmentation Method for Few-Shot Learning.

Few-shot learning (FSL) aims to learn novel concepts quickly from a few novel labeled samples with the transferable knowledge learned from base dataset. The existing FSL methods usually treat each sample as a single feature point in embedding space and classify through one single comparison task. However, the few-shot single feature points on the novel meta-testing episode are still vulnerable to noise easily although with the good transferable knowledge, because the novel categories are never seen on base dataset.

A Convex Model for Support Vector Distance Metric Learning.

Distance metric learning (DML) aims to learn a distance metric to process the data distribution. However, most of the existing methods are k NN DML methods and employ the k NN model to classify the test instances. The drawback of k NN DML is that all training instances need to be accessed and stored to classify the test instances, and the classification performance is influenced by the setting of the nearest neighbor number k .

An Efficient Image Categorization Method With Insufficient Training Samples.

Image classification is an important part of pattern recognition. With the development of convolutional neural networks (CNNs), many CNN methods are proposed, which have a large number of samples for training, which can have high performance. However, there may exist limited samples in some real-world applications.

A Transfer Learning-Based Multi-Instance Learning Method With Weak Labels.

In multi-instance learning (MIL), labels are associated with bags rather than the instances in the bag. Most of the previous MIL methods assume that each bag has the actual label in the training set. However, from the process of labeling work, the label of a bag is always evaluated by the calculation of the labels obtained from a number of labelers.

A Sphere-Description-Based Approach for Multiple-Instance Learning.

Multiple-instance learning (MIL) is a generalization of supervised learning which addresses the classification of bags. Similar to traditional supervised learning, most of the existing MIL work is proposed based on the assumption that a representative training set is available for a proper learning of the classifier. That is to say, the training data can appropriately describe the distribution of positive and negative data in the testing set.

A Maximum Margin Approach for Semisupervised Ordinal Regression Clustering.

Ordinal regression (OR) is generally defined as the task where the input samples are ranked on an ordinal scale. OR has found a wide variety of applications, and a great deal of work has been done on it. However, most of the existing work focuses on supervised/semisupervised OR classification, and the semisupervised OR clustering problems have not been explicitly addressed.

A similarity-based classification framework for multiple-instance learning.

Multiple-instance learning (MIL) is a generalization of supervised learning that attempts to learn useful information from bags of instances. In MIL, the true labels of instances in positive bags are not available for training. This leads to a critical challenge, namely, handling the instances of which the labels are ambiguous (ambiguous instances).

Improved algorithm for phase-to-height mapping in phase measuring profilometry.

An improved algorithm for phase-to-height mapping in phase-measuring profilometry (PMP) is proposed, in which the phase-to-height mapping relationship is no longer restricted to the condition that the optical axes of the imaging system must be orthogonal to the reference plane in the basic PMP. Only seven coefficients independent of the coordinate system need to be calibrated, and the system calibration can be accomplished using only two different gauge blocks, instead of more than three different standard planes. With the proposed algorithm, both the phase measurement and system calibration can be completed simultaneously, which makes the three-dimensional (3-D) measurement faster and more flexible.