In this paper, we argue that ordering prenominal modifiers – typically pursued as a supervised modeling task – is particularly wellsuited to semi-supervised approaches. By relying on automatic parses to extract noun phrases, we can scale up the training data by orders of magnitude. This minimizes the predominant issue of data sparsity that has informed most previous approaches. We compare sever...

We analyze the combination of online learning and semisupervised learning. Online learning is an approach to supervised machine learning, which is effective at processing a large amount of training data. Semi-supervised learning is employed when a large amount of unlabeled data is available. While both setting occurs frequently in real applications of machine learning, thus far, their theoretic...

We compare the accuracy of a statistical parse ranking model trained from a fully-annotated portion of the Susanne treebank with one trained from unlabeled partially-bracketed sentences derived from this treebank and from the Penn Treebank. We demonstrate that confidence-based semi-supervised techniques similar to self-training outperform expectation maximization when both are constrained by pa...

Non-negative spectrogram factorization algorithms such as probabilistic latent component analysis (PLCA) have been shown to be quite powerful for source separation. When training data for all of the sources are available, it is trivial to learn their dictionaries beforehand and perform supervised source separation in an online fashion. However, in many real-world scenarios (e.g. speech denoisin...

We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in limited supply. We consider the specific case of classifying skin lesions as either malignant or benign. In this setting, the proposed approach – the semi-supervised, denoising adversarial autoencoder – is able to utilis...

In the class based image segmentation problem, one of the major concerns is to provide large training data for learning complex graphical models. To alleviate the labeling effort, a concise annotation approach working on bounding boxes is introduced. The main idea is to leverage the knowledge learned from a few object contours for the inference of unknown contours in bounding boxes. To this end...

The aim of Named Entity Recognition (NER) is to identify references of named entities in unstructured documents, and to classify them into pre-defined semantic categories. NER often aids from added background knowledge in the form of gazetteers. However using such a collection does not deal with name variants and cannot resolve ambiguities associated in identifying the entities in context and a...

Semi-supervised learning plays a significant role in multi-class classification, where a small number of labeled data are more deterministic while substantial unlabeled data might cause large uncertainties and potential threats. In this paper, we distinguish the label fitting of labeled and unlabeled training data through a probabilistic vector with an adaptive parameter, which always ensures t...

In this paper, we first present a self-training semi-supervised support vector machine (SVM) algorithm and its corresponding model selection method, which are designed to train a classifier with small training data. Next, we prove the convergence of this algorithm. Two examples are presented to demonstrate the validity of our algorithm with model selection. Finally, we apply our algorithm to a ...

By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML data [23] has been available on intra and internet. These electronic data are heterogeneous collection of ill-structured data that have no rigid structures, and often called semi-structured data [1]. Hence, there have been increasing demands for automatic methods for extracting usef...