Eager learners such as neural networks, decision trees, and naïve Bayes classifiers construct a single model from the training data before observing any test set instances. In contrast, lazy learners such as Knearest neighbor consider a test set instance before they generalize beyond the training data. This allows making predictions from only a specific selection of instances most similar to th...

This work proposes and evaluates a Nearest-Neighbor Method to substitute missing values in datasets formed by continuous attributes. In the substitution process, each instance containing missing values is compared with complete instances, and the closest instance is used to assign the attribute missing value. We evaluate this method in simulations performed in four datasets that are usually emp...

Part-MOT, a one-stage anchor-free architecture which unifies the object identification representation and detection in one task for visual tracking is presented. For representation, position relevant feature obtained using center-ness information, takes advantage of ideal to encode map as instance-aware embedding. To adapt object's movement, clustering-based method get global instance introduce...

Clustering technology has been applied in numerous applications. It can enhance the performance of information retrieval systems, it can also group Internet users to help improve the click-through rate of on-line advertising, etc. Over the past few decades, a great many data clustering algorithms have been developed, including K-Means, DBSCAN, Bi-Clustering and Spectral clustering, etc. In rece...

Data in the real world is far from being perfect. The appearance of noise a common issue that arises limitations data acquisition mechanisms and human knowledge. In classification, label will hinder performance almost all classifiers, inducing bias built model. While has recently attracted researchers’ attention standard it only begun to be studied multiple instance classification. this work, w...

We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks. This framework can be used to model the traditional MIL definition as well as more general MIL definitions. Different levels of ambiguity – the portion of positive instances in a bag – can be explored in weakly supervised data. To train these models, we propose a discriminative maxmargin learning a...

Multiple-Instance Learning via Embedded Instance Selection (MILES) is a recently proposed multiple-instance (MI) classification algorithm that applies a single-instance base learner to a propositionalized version of MI data. However, the original authors consider only one single-instance base learner for the algorithm — the 1-norm SVM. We present an empirical study investigating the efficacy of...

Automatic screening of Age-related Macular Degeneration (AMD) is important for both patients and ophthalmologists. The major sign of contracting AMD at the early stage is the appearance of drusen, which are the accumulation of extracellular material and appear as yellow-white spots on the retina. In this paper, we propose an effective approach for drusen segmentation towards AMD screening. The ...