The low accuracy rates of text–shape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing ink. Feature based recognition is a common approach for text–shape division....

Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To acco...

In visual object class recognition it is difficult to densely sample the set of positive examples. Therefore, frequently there will be areas of the feature space that are sparsely populated, in which uncommon examples are hard to disambiguate from surrounding negatives without overfitting. Boosting in particular struggles to learn optimal decision boundaries in the presence of such hard and amb...

Boosting algorithms are a means of building a strong ensemble classiier by aggregating a sequence of weak hypotheses. In this paper we consider three of the best-known boosting algorithms: Adaboost 8], Logitboost 10] and Brownboost 7]. These algorithms are adaptive, and work by maintaining a set of example and class weights which focus the attention of a base learner on the examples that are ha...

We consider the problem of content-based automated tag learning. In particular, we address semantic variations (sub-tags) of the tag. Each video in the training set is assumed to be associated with a sub-tag label, and we treat this sub-tag label as latent information. A latent learning framework based on LogitBoost is proposed, which jointly considers both the tag label and the latent sub-tag ...

The standard by which binary classifiers are usually judged, misclassification error, assumes equal costs of misclassifying the two classes or, equivalently, classifying at the 1/2 quantile of the conditional class probability function P[y = 1|x]. Boosted classification trees are known to perform quite well for such problems. In this article we consider the use of standard, off-the-shelf boosti...

Crop and weeds identification is of important steps towards the development efficient automotive weed control systems. The higher accuracy plant detection classification, performance weeding machine. In this study, capability two popular boosting methods including Adaboost.M1 LogitBoost algorithms was evaluated to enhance classification four classifiers, namely Multi-Layer Perceptron (MLP), k-N...

Data generated from modern applications and the internet in healthcare is extensive rapidly expanding. Therefore, one of significant success factors for any application understanding extracting meaningful information using digital analytics tools. These tools will positively impact application's performance handle challenges that can be faced to create highly consistent, logical, information-ri...