In classification, semi-supervised learning occurs when a large amount of unlabeled data is available with only a small number of labeled data. In such a situation, how to enhance predictability of classification through unlabeled data is the focus. In this article, we introduce a novel large margin semi-supervised learning methodology, using grouping information from unlabeled data, together w...

 Investigate determinantal point processes (DPPs) for discriminative subset selection  Proposemargin based parameter estimation to explicitly track errors in selecting subsets  Balance different types of evaluation metrics, e.g., precision and recall  Improve modeling flexibility by multiple-kernel based parameterization  Attain state-of-the-art performance on the tasks of video and docume...

Neural language models (NLMs) are generative, and they model the distribution of grammatical sentences. Trained on huge corpus, NLMs are pushing the limit of modeling accuracy. Besides, they have also been applied to supervised learning tasks that decode text, e.g., automatic speech recognition (ASR). By re-scoring the n-best list, NLM can select grammatically more correct candidate among the l...

We present the Convex Polytope Machine (CPM), a novel non-linear learning algorithm for large-scale binary classification tasks. The CPM finds a large margin convex polytope separator which encloses one class. We develop a stochastic gradient descent based algorithm that is amenable to massive data sets, and augment it with a heuristic procedure to avoid sub-optimal local minima. Our experiment...

Large Relative Margin and Applications Pannagadatta K. Shivaswamy Over the last decade or so, machine learning algorithms such as support vector machines, boosting etc. have become extremely popular. The core idea in these and other related algorithms is the notion of large margin. Simply put, the idea is to geometrically separate two classes with a large separation between them; such a separat...

Two attractive advantages of SVM are the ideas of kernels and of large margin. As a linear learning machine, the original pocket algorithm can handle both linearly and nonlinearly separable problems. In order to improve its classification ability and control its generalization, we generalize the original pocket algorithm by using kernels and adding a margin criterion, and propose its kernel and...

Neural language models (NLMs) are generative, and they model the distribution of grammatical sentences. Trained on huge corpus, NLMs are pushing the limit of modeling accuracy. Besides, they have also been applied to supervised learning tasks that decode text, e.g., automatic speech recognition (ASR). By re-scoring the n-best list, NLM can select grammatically more correct candidate among the l...

Linear metric learning is a widely used methodology to learn a dissimilarity function from a set of similar/dissimilar example pairs. Using a single metric may be a too restrictive assumption when handling heterogeneous datasets. Recently, local metric learning methods have been introduced to overcome this limitation. However, they are subjects to constraints preventing their usage in many appl...

Large margin classifiers have been shown to be very useful in many applications. The Support Vector Machine is a canonical example of large margin classifiers. Despite their flexibility and ability in handling high dimensional data, many large margin classifiers have serious drawbacks when the data are noisy, especially when there are outliers in the data. In this paper, we propose a new weight...