This paper presents a semi-supervised machine-learning approach to predicting whether students will be successful in solving problem-solving tasks within narrative-centered learning environments. Results suggest the approach often outperforms standard supervised learning methods.

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 show that for linear classifiers defined by convex marginbased surrogate losses that are monotonically decreasing, it is impossible to construct any semi-supervised approach that is able to guarantee an improvement over the supervised classifier measured by this surrogate loss. For non-monotonically decreasing loss functions, we demonstrate safe improvements are possible.

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...

In this paper, we address the semi-supervised learning problem when there is a small amount of labeled data augmented with pairwise constraints indicating whether a pair of examples belongs to a same class or different classes. We introduce a discriminative learning approach that incorporates pairwise constraints into the conventional margin-based learning framework. We also present an efficien...

Most work in NLP analysing microblogs focuses on textual content thus neglecting temporal and spatial information. We present a new interdisciplinary method for emotion classification that combines linguistic, temporal, and spatial information into a single metric. We create a graph of labeled and unlabeled tweets that encodes the relations between neighboring tweets with respect to their emoti...