Sentiment analysis is a person's opinion or view of particular object that produces positive, negative, neutral sentiments. The government's effort during the COVID-19 pandemic to call for implementation booster vaccination program public. Based on this, it several public sentiments, some which are uploaded Twitter social media platform, generate positive and negative To find out classification...

De novo structure determination of proteins is a significant research issue of bioinformatics. Biochemical procedures for protein structure determination are costly. Use of different pattern classification techniques are proved to ease this task. In this article, the secondary structure prediction task has been mapped into a three-class problem of pattern classification, where the classes are h...

An investigation has been conducted on two well known similarity-based learning approaches to text categorization. This includes the k-nearest neighbor (kNN) classifier and the Rocchio classifier. After identifying the weakness and strength of each technique, we propose a new classifier called the kNN model-based classifier by unifying the strengths of k-NN and Rocchio classifier and adapting t...

K-nearest neighbor rule (KNN) is the wellknown non-parametric technique in the statistical pattern classification, owing to its simplicity, intuitiveness and effectiveness. In this paper, we firstly review the related works in brief and detailedly analyze the sensitivity issue on the choice of the neighborhood size k, existed in the KNN rule. Motivated by the problem, a novel dual weighted voti...

Nearest neighbor classification expects the class conditional probabilities to be locally constant. The assumption becomes invalid in high dimension due to the curse−of−dimensionality. Severe bias can be introduced under this condition when using nearest neighbor rule. We propose an adaptive nearest neighbor classification method ¡°indecisive classifier¡± to minimize bias and variance by avoidi...

Nearest neighbor search is a fundamental computational building block in computer vision, graphics, data mining, machine learning, and many other subfields. As an example, consider a simple k-nearestneighbor-classifier which, for each point predicts its class by the a majority vote over its neighbors’ classes. As simplistic as this classifier sounds, it actually performs very well in many scena...

We describe a framework for learning an object classifier from a single example, by emphasizing relevant dimensions using available examples of related classes. Learning to accurately classify objects from a single training example is often unfeasible due to overfitting effects. However, if the instance representation provides that the distance between each two instances of the same class is sm...

We describe a framework for learning an object classifier from a single example. This goal is achieved by emphasizing the relevant dimensions for classification using available examples of related classes. Learning to accurately classify objects from a single training example is often unfeasible due to overfitting effects. However, if the instance representation provides that the distance betwe...