In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear discriminative models learned on few gallery images provides coarse separation of probe images into identities, ...

Obtaining labeled data for training classifiers is an expensive task that must be done in any new application. It is yet even more expensive for structured models, such as Conditional Random Fields, where some notion of coherence in the labeled data must be maintained. To address this issue, semi-supervised methods are often applied to reduce the needed number of labeled examples to train adequ...

The socio-economic development of largely subsistence-oriented rural areas is a prominent policy objective in Laos. Whereas in the surrounds of major urban centres and regions with favourable infrastructure and market access a dynamic industrial and service sector have emerged, most of the countryside remains economically undeveloped. Cultivation of commercial tree species is acknowledged as a ...

We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, that is the surface forms of morphemes. We extend a recent segmentation approach based on conditional random fields from purely supervised to semi-supervised learning by exploiting available unsupervised segmentation techniques. We integrate the unsupervised techniques into the conditional random f...

This paper introduces a new semi-supervised Bayesian approach to hyperspectral image segmentation. The algorithm mainly consists of two steps: (a) semi-supervised learning, by using the LORSAL algorithm to infer the class distributions, followed by (b) segmentation, by inferring the labels from a posterior density built on the learned class distributions and on a Markov random field. Active lab...

We identify two properties that for P-selective sets are effectively computable. Namely we show that, for any P-selective set, finding a string that is in a given length’s top Toda equivalence class (very informally put, a string from Σ that the set’s P-selector function declares to be most likely to belong to the set) is FP p 2 computable, and we show that each P-selective set contains a weakl...

Abstract Semi-supervised learning (SSL) has become an interesting research area due to its capacity for in scenarios where both labeled and unlabeled data are available. In this work, we focus on the task of transduction—when objective is label all presented learner—with a mean-field approximation Potts model. Aiming at particular study how classification results depend β find that optimal phas...

In clinical practice, sensitivity of malignant cells to a given immunotoxin remains hypothetical, since standard test systems such as the protein synthesis inhibition assay or the cloning assay are not appropriate. This study evaluated the feasibility of a semi-routine procedure based on dye exclusion assay enumerating the percentage of living cells after fluorescein diacetate-propidium iodide ...

Methicillin-resistant Staphylococcus aureus (MRSA) is a significant cause of hospital-associated pneumonia (HAP). The rapid identification of MRSA would be beneficial for early diagnosis. The study aimed to evaluate a multilocus, fluorescence-based PCR assay based on the detection of mecA and nuc genes for identification of S. aureusin lower respiratory tract (LRT) specimens. Sensitivity and sp...

There is rich knowledge encoded in online web data. For example, punctuation and entity tags in Wikipedia data define some word boundaries in a sentence. In this paper we adopt partial-label learning with conditional random fields to make use of this valuable knowledge for semi-supervised Chinese word segmentation. The basic idea of partial-label learning is to optimize a cost function that mar...