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Found 14 papers, 5 papers with code
On Statistical Learning of Branch and Bound for Vehicle Routing Optimization
Recently, machine learning of the branch and bound algorithm has shown promise in approximating competent solutions to NP-hard problems.
UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring
Among them is designing a visualization aided anomaly detection (VAAD), a type of software that aids analysts by providing UN-AVOIDS' detection algorithm (running in a back engine), NCDF visualization space (rendered to plots), along with other conventional methods of visualization in the original feature space, all of which are linked in one interactive environment.
Classifier Calibration: with application to threat scores in cybersecurity
A calibrator is a function that maps the arbitrary classifier score, of a testing observation, onto $[0, 1]$ to provide an estimate for the posterior probability of belonging to one of the two classes.
Machine Learning in Precision Medicine to Preserve Privacy via Encryption
Precision medicine is an emerging approach for disease treatment and prevention that delivers personalized care to individual patients by considering their genetic makeups, medical histories, environments, and lifestyles.
Assessment of Multiple-Biomarker Classifiers: fundamental principles and a proposed strategy
Some resources available in the literature for implementing this approach are reviewed.
Method and System for Image Analysis to Detect Cancer
(1) We develop a novel algorithm for image enhancement so that mammograms acquired from any digital mammography machine look qualitatively of the same clarity to radiologists' inspection; and is quantitatively standardized for the detection algorithms.
Estimating the standard error of cross-Validation-Based estimators of classifier performance
First, we analyze the variance of the Cross Validation (CV)-based estimators used for estimating the performance of classification rules.
On The Smoothness of Cross-Validation-Based Estimators Of Classifier Performance
We show that the latter is the only among the three versions that is "smooth" and hence looks mathematically like estimating the mean performance of the classification rules.
Prudence When Assuming Normality: an advice for machine learning practitioners
In a binary classification problem the feature vector (predictor) is the input to a scoring function that produces a decision value (score), which is compared to a particular chosen threshold to provide a final class prediction (output).
Machine Learning Assessment: implications to cybersecurity
This chapter provides a concise review of this literature to establish a coherent theoretical framework for these methods that can estimate both the error rate (a one-sample statistic) and the AUC (a two-sample statistic).
Machine Learning Construction: implications to cybersecurity
We mean by \textit{construction} designing or inventing an appropriate algorithm that learns from the input data and achieves a good performance according to some optimality criterion.
Nested Cavity Classifier: performance and remedy
We coin the term Nested Cavity Discriminant Analysis (NCDA) for the resulting classifier.
Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis
Recognizing a piece of writing as a poem or prose is usually easy for the majority of people; however, only specialists can determine which meter a poem belongs to.
Ranked #1 on
Poem meters classification
on PCD
Matlab vs. OpenCV: A Comparative Study of Different Machine Learning Algorithms
The best of two worlds can be achieved by exploring using Matlab or similar environments to select the most successful algorithm; then, implementing the selected algorithm using OpenCV or similar environments to gain a speed factor.
