Performance prediction has gained increasing attention in the IR field since the half of the past decade and has become an established research topic in the field. The present work restates the problem in the area of Collaborative Filtering (CF), where it has barely been researched so far. We investigate the adaptation of clarity-based query performance predictors to predict neighbor performanc...

This paper presents a Mandarin audio-visual recognition system dealing with noisy and emotional speech signal. In the proposed approach, we extract the visual features of the lips. These features are very important to the recognition system especially in noisy condition or with emotional effects. In this recognition system, we propose to use the weighted-discrete KNN as the classifier and compa...

The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining te...

This work, concerning paraphrase identification task, on one hand contributes to expanding deep learning embeddings to include continuous and discontinuous linguistic phrases. On the other hand, it comes up with a new scheme TF-KLD-KNN to learn the discriminative weights of words and phrases specific to paraphrase task, so that a weighted sum of embeddings can represent sentences more effective...

This study initially seeks to identify the most optimal supervised learning algorithm be used in predicting perception of teacher performance, and then evaluate its performance indicators that validate predictive capacity. For this, Matlab R2021a software is used; experimental results determine K-Nearest Neighbor Weighted (Weighted KNN) will correct 98.10% teaching this has been validated by ca...

We combine K-Nearest Neighbors (KNN) with genetic algorithm (GA) for photometric redshift estimation of quasars, short GeneticKNN, which is a weighted KNN approach supported by GA. This has two improvements compared to KNN: one the feature GA; another that predicted not average K neighbors but median and mean redshifts neighbors, i.e. $p\times z_{median} + (1-p)\times z_{mean}$. Based on SDSS S...

Rényi entropy is an information-theoretic measure of randomness which is fundamental to several applications. Several estimators of Rényi entropy based on k-nearest neighbor (kNN) based distances have been proposed in literature. For d-dimensional densities f , the variance of these Rényi entropy estimators of f decay as O(M), whereM is the sample size drawn from f . On the other hand, the bias...

Electroencephalograph (EEG) is nothing but the collection of electrical signals of brain. EEG contains the most significant information about the activities of the brain. In this paper, the detection and classification of epileptic seizures in EEG signals is done with the help of Fuzzy Mutual Information (FMI) and Weighted KNN Classifier. Initially, the dimension of the EEG is reduced with the ...

Pattern classification is a core research area and a main task in pattern recognition. A classifier induced by machine learning algorithms maps an unlabeled instance to a label using internal data structures. In this paper we experiment first by changing the k value of nearest neighbors from 3 to 15 and compare the accuracy of two classifiers on various training and test sets. The results show ...

This paper proposes a method to identify flooding attacks in real-time, based on anomaly detection by genetic weighted KNN (K-nearest-neighbor) classifiers. A genetic algorithm is used to train an optimal weight vector for features; meanwhile, an unsupervised clustering algorithm is applied to reduce the number of instances in the sampling dataset, in order to shorten training and execution tim...