We demonstrate that a state-of-the-art parser can be built using only a lexical tagging model and a deterministic grammar, with no explicit model of bi-lexical dependencies. Instead, all dependencies are implicitly encoded in an LSTM supertagger that assigns CCG lexical categories. The parser significantly outperforms all previously published CCG results, supports efficient and optimal A∗ decod...

Even though the accuracy of predictions made by speech emotion recognition (SER) systems is increasing in precision, little is known about the confidence of the predictions. To shed some light on this, we propose a confidence measure for SER systems based on semi-supervised learning. During the semi-supervised learning procedure, five frequently used databases with manually created confidence l...

This paper presents Correlated Nyström Views (XNV), a fast semi-supervised algorithm for regression and classification. The algorithm draws on two main ideas. First, it generates two views consisting of computationally inexpensive random features. Second, multiview regression, using Canonical Correlation Analysis (CCA) on unlabeled data, biases the regression towards useful features. It has bee...

A semi-supervised segmentation method using a single atlas is presented in this paper. Traditional atlas-based segmentation suffers from either a strong bias towards the selected atlas or the need for manual effort to create multiple atlas images. Similar to semi-supervised learning in computer vision, we study a method which exploits information contained in a set of unlabelled images by mutua...

Intrusion Detection Systems (IDSs) which have been deployed in computer networks to detect a wide variety of attacks are suffering how to manage of a large number of triggered alerts. Thus, reducing false alarms efficiently has become the most important issue in IDS. In this paper, we introduce the semi-supervised learning mechanism to build an alert filter, which will reduce up to 85% false al...

Meta-learning involves the construction of a classifier that predicts the performance of another classifier. Previously proposed approaches do this by making a single prediction (such as the expected accuracy) for a complete data set. We suggest modifying this framework so that the meta-classifier predicts for each data point in the data set whether a particular base-classifier will classify it...

A novel semi-supervised learning approach based on statistical physics is proposed in this paper. We treat each data point as an Ising spin and the interaction between pairwise spins is captured by the similarity between the pairwise points. The labels of the data points are treated as the directions of the corresponding spins. In semi-supervised setting, some of the spins have fixed directions...

We propose a novel semi-supervised learning scheme using adaptive interpolation on multiple one-dimensional (1-D) embedded data. For a give high dimensional data set, we smoothly map it onto several different one-dimensional (1-D) sequences, so that the labeled subset is converted to a 1-D subset for each of these sequences. Applying the cubic interpolation of the labeled subset, we obtain a su...

We propose to use semi-supervised learning methods to classify evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate either favorable or unfavorable opinions towards a specific subject. Due to its characteristics, the semisupervised method that we use can classify evaluative expressions in a corpus by their polarities. This can be accomplishe...

For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first increase the error rate before decreasing it. This, possibly counterintuitive, phenomenon is known as peaking. In this work, we observe that a similar but more pronounced version of this phenomenon also occurs in the semi...