A new machine learning algorithm for the diagno sis of cardiac arrhythmia from standard lead ECG recordings is presented The algorithm is called VFI for Voting Feature Intervals VFI is a supervised and inductive learning algorithm for inducing classi cation knowledge from examples The input to VFI is a train ing set of records Each record contains clinical mea surements from ECG signals and som...

This paper presents a study on the performance of transformed domain features in Devnagari digit recognition. In this research the recognition performance is measured from features obtained in direct pixel value, Fourier Transform, Discrete Cosine Transform, Gaussian Pyramid, Laplacian Pyramid, Wavelet Transform and Curvelet Transform using classification schemes: Feed Forward, Function Fitting...

|We propose a new method of classiication built as a cascade of a distributed learner and a local learner. The distributed learner generalizes to learn the \rule" and the local learner learns the \exceptions" not covered by the \rule." We show how such a system can be trained using cross-validation. We use a multi-layer perceptron with sigmoidal hidden units as the rule-learner and a k-nearest ...

How were the most underprivileged women mobilized after suffrage? Newly enfranchised faced a multitude of barriers to voting, and this was especially case for working-class women. We theorize that more likely acquire civic attitudes information through ties with neighbors same class than men or privileged classes. Working-class women’s typical employment domestic responsibilities provided oppor...

This study analyzes the performance of hybrid methods in improving accuracy on imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 Bandung City. The attributes used this consist Total Population, Male, Elementary School Graduation, Junior High College Rainfall, Average Temperature, Humidity, Male Cases, Number and Class. research combines five Machine Learning methods, su...

Concept lattice model, the core structure in Formal Concept Analysis, has been successfully applied in software engineering and knowledge discovery. In this paper, we integrate the simple base classifier (Naïve Bayes or Nearest Neighbor) into each node of the concept lattice to form a new composite classifier. We develop two new classification systems, CLNB and CLNN, that employ efficient const...

We propose a novel scoring concept for visual place recognition based on nearest neighbor descriptor voting and demonstrate how the algorithm naturally emerges from the problem formulation. Based on the observation that the number of votes for matching places can be evaluated using a binomial distribution model, loop closures can be detected with high precision. By casting the problem into a pr...

This paper presents the results of the application of an instance based learning algorithm k Nearest Neighbor Method on Fea ture Projections k NNFP to text categorization and compares it with k Nearest Neighbor Classi er k NN k NNFP is similar to k NN ex cept it nds the nearest neighbors according to each feature separately Then it combines these predictions using a majority voting This prop er...

In this paper, we propose a new, effective, and unified scoring method for local feature-based image retrieval. The proposed scoring method is derived by solving the large-scale image retrieval problem as a classification problem with a large number of classes. The resulting proposed score is based on the ratio of the probability density function of an object model to that of a background model...

Do print media significantly impact political attitudes and party identification? To examine this question, we draw on a rare quasi-natural experiment that occurred when The Sun, a right-leaning UK tabloid, shifted its support to the Labour party in 1997 and back to the Conservative party in 2010. We compared changes in party identification and political attitudes among Sun readers with non-rea...