Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks natural language processing. However, despite widespread use NER models, they still require a large-scale labeled data set, which incurs heavy burden due to manual annotation. Domain adaptation promising solutions this problem, where rich from relevant source domai...

We present a general framework of semi-supervised dimensionality reduction for manifold learning which naturally generalizes existing supervised and unsupervised learning frameworks which apply the spectral decomposition. Algorithms derived under our framework are able to employ both labeled and unlabeled examples and are able to handle complex problems where data form separate clusters of mani...

Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be used for semi-supervised regression on high-dimensional data. We propose an active learning strategy based on entropy minimization and a maximum likelihood model selection method. Furthermore, we show how a recent genera...

This paper presents a unified framework for intra-view and inter-view constraint propagation on multi-view data. Pairwise constraint propagation has been studied extensively, where each pairwise constraint is defined over a pair of data points from a single view. In contrast, very little attention has been paid to inter-view constraint propagation, which is more challenging since each pairwise ...

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the of CNNs depends heavily on a large amount training data. The insufficiency labeled SAR images limits and even invalidates some ATR methods. Furthermore, under few data, many existing are ineffective. To address these challenges, we propose Semi-s...

Abstract Multiple instance boosting (MILBoost) is a framework which uses multiple learning (MIL) with technique to solve the problems regarding weakly labeled inexact data. This paper proposes an enhanced framework—evolutionary MILBoost (EMILBoost) utilizes differential evolution (DE) optimize combination of weak classifier or estimator weights in framework. A standard MIL dataset MUSK and bina...

In this paper we propose a framework for supervised and semi-supervised learning based on reformulating the learning problem as a regularized Fredholm integral equation. Our approach fits naturally into the kernel framework and can be interpreted as constructing new data-dependent kernels, which we call Fredholm kernels. We proceed to discuss the “noise assumption” for semi-supervised learning ...

Semi-supervised learning has achieved remarkable success in the past few years at harnessing the power of unlabeled data and tackling domains where few labeled data examples exist. We test the hypothesis that deep semisupervised architectures learn general representations. We combine two well-known techniques for semi-supervised and transfer learning, ladder networks and progressive neural netw...

We supplement WordNet entries with information on the subjectivity of its word senses. Supervised classifiers that operate on word sense definitions in the same way that text classifiers operate on web or newspaper texts need large amounts of training data. The resulting data sparseness problem is aggravated by the fact that dictionary definitions are very short. We propose a semi-supervised mi...