Vision-based autonomous driving requires classifying each pixel as corresponding to road or not, which can be addressed using semantic segmentation. Semantic segmentation works well when used with a fully supervised model, but in practice, the required work of creating pixel-wise annotations is very expensive. Although weakly supervised segmentation addresses this issue, most methods are not de...

This study implements a vector space model approach to measure the sentiment orientations of words. Two representative vectors for positive/negative polarity are constructed using high-dimensional vector space in both an unsupervised and a semisupervised manner. A sentiment orientation value per word is determined by taking the difference between the cosine distances against the two reference v...

Sentiment classification is one of the most extensively studied problems in sentiment analysis and supervised learning methods, which require labeled data for training, have been proven quite effective. However, supervised methods assume that the training domain and the testing domain share the same distribution; otherwise, accuracy drops dramatically. Although this does not pose problems when ...

Contrary to the previous beliefs that all arrived streaming data are labeled and the class labels are immediately available, we propose a Semi-supervised classification algorithm for data streams with concept drifts and UNlabeled data, called SUN. SUN is based on an evolved decision tree. In terms of deviation between history concept clusters and new ones generated by a developed clustering alg...

Sentiment analysis of documents aims to characterise the positive or negative sentiment expressed in documents. It has been formulated as a supervised classification problem, which requires large numbers of labelled documents. Semi-supervised sentiment classification using limited documents or words labelled with sentiment-polarities are approaches to reducing labelling cost for effective learn...

We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effect...

Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold. The graph Laplacian of such a graph is used in several machine learning methods like semi-supervised learning, dimensionality reduction and clustering. In this paper we determine the pointwise limit of three d...

Semi–supervised clustering is an attempt to reconcile clustering (unsupervised learning) and classification (supervised learning, using prior information on the data.) These two modes of data analysis are combined in a parameterized model, the parameter θ∈ [0, 1] is the weight attributed to the prior information, θ = 0 corresponding to clustering, and θ = 1 to classification. The results (clust...

In many machine learning domains, there is a large supply of unlabeled data but limited labeled data, which can be expensive to generate. Consequently, semi-supervised learning, learning from a combination of both labeled and unlabeled data, has become a topic of significant recent interest. Our research focus is on semi-supervised clustering, which uses a small amount of supervised data in the...

Semi-supervised learning has witnessed increasing interest in the past decade. One common assumption behind semi-supervised learning is that the data labels should be sufficiently smooth with respect to the intrinsic data manifold. Recent research has shown that the features also lie on a manifold. Moreover, there is a duality between data points and features, that is, data points can be classi...