Semi-supervised Learning Tasks Research Articles

Automatic Classification of Unstructured Blog Text

Automatic classification of blog entries is generally treated as a semi-supervised machine learning task, in which the blog entries are automatically assigned to one of a set of pre-defined classes based on the features extracted from their textual content. This paper attempts automatic classification of unstructured blog entries by following pre-processing steps like tokenization, stop-word elimination and stemming; statistical techniques for feature set extraction, and feature set enhancement using semantic resources followed by modeling using two alternative machine learning models—the na?ve Bayesian model and the artificial neural network model. Empirical evaluations indicate that this multi-step classification approach has resulted in good overall classification accuracy over unstructured blog text datasets with both machine learning model alternatives. However, the na?ve Bayesian classification model clearly out-performs the ANN based classification model when a smaller feature-set is available which is usually the case when a blog topic is recent and the number of training datasets available is restricted.

Automatic Text Classification: A Technical Review

Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering and text sentiment mining. This paper explains the generic strategy for automatic text classification and surveys existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application.

A Regularized Maximum Figure-of-Merit (rMFoM) Approach to Supervised and Semi-Supervised Learning

We propose a regularized extension to supervised maximum figure-of-merit learning to improve its generalization capability and successfully extend it to semi-supervised learning. The proposed method can be used to approximate any objective function consisting of the commonly used performance metrics. We first derive detailed learning algorithms for supervised learning problems and then extend it to more general semi-supervised scenarios, where only a small part of the training data is labeled. The effectiveness of the proposed approach is justified by several text categorization experiments on different datasets. The novelty of this paper lies in several aspects: 1) Tikhonov regularization is used to alleviate potential overfitting of the maximum figure-of-merit criteria; 2) the regularized maximum figure-of-merit algorithm is successfully extended to semi-supervised learning tasks; 3) the proposed approach has good scalability to large-scale applications.

Semi-supervised Gaussian process latent variable model with pairwise constraints

In machine learning, Gaussian process latent variable model (GP-LVM) has been extensively applied in the field of unsupervised dimensionality reduction. When some supervised information, e.g., pairwise constraints or labels of the data, is available, the traditional GP-LVM cannot directly utilize such supervised information to improve the performance of dimensionality reduction. In this case, it is necessary to modify the traditional GP-LVM to make it capable of handing the supervised or semi-supervised learning tasks. For this purpose, we propose a new semi-supervised GP-LVM framework under the pairwise constraints. Through transferring the pairwise constraints in the observed space to the latent space, the constrained priori information on the latent variables can be obtained. Under this constrained priori, the latent variables are optimized by the maximum a posteriori (MAP) algorithm. The effectiveness of the proposed algorithm is demonstrated with experiments on a variety of data sets.

Learning with $\ell ^{1}$-graph for image analysis

The graph construction procedure essentially determines the potentials of those graph-oriented learning algorithms for image analysis. In this paper, we propose a process to build the so-called directed l1-graph, in which the vertices involve all the samples and the ingoing edge weights to each vertex describe its l1-norm driven reconstruction from the remaining samples and the noise. Then, a series of new algorithms for various machine learning tasks, e.g., data clustering, subspace learning, and semi-supervised learning, are derived upon the l1-graphs. Compared with the conventional k-nearest-neighbor graph and epsilon-ball graph, the l1-graph possesses the advantages: (1) greater robustness to data noise, (2) automatic sparsity, and (3) adaptive neighborhood for individual datum. Extensive experiments on three real-world datasets show the consistent superiority of l1-graph over those classic graphs in data clustering, subspace learning, and semi-supervised learning tasks.

Semi-supervised learning from unbalanced labeled data: An improvement

We present a great improvement while performing semi-supervised learning tasks from training data sets when only a small fraction of the data pairs is labeled. In particular, we propose a novel decision strategy based on normalized model outputs. We give the explanation why the normalization step helps. The paper compares performances of two popular semi-supervised approaches (Consistency Method and Harmonic Gaussian Model) on the unbalanced and balanced labeled data by using normalization of the models' outputs and without it. Experiments on text categorization problems suggest significant improvements in classification performances for models that use normalized outputs as a basis for final decision.