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Found 13 papers, 0 papers with code
Weighted Distance Nearest Neighbor Condensing
The problem of nearest neighbor condensing has enjoyed a long history of study, both in its theoretical and practical aspects.
Using Deepfake Technologies for Word Emphasis Detection
In this work, we consider the task of automated emphasis detection for spoken language.
Predicting subscriber usage: Analyzing multi-dimensional time-series using Convolutional Neural Networks
Companies operating under the subscription model typically invest significant resources attempting to predict customer's feature usage.
Functions with average smoothness: structure, algorithms, and learning
Rather than using the Lipschitz constant as the regularizer, we define a local slope at each point and gauge the function complexity as the average of these values.
Nested Barycentric Coordinate System as an Explicit Feature Map
We propose a new embedding method which is particularly well-suited for settings where the sample size greatly exceeds the ambient dimension.
Apportioned Margin Approach for Cost Sensitive Large Margin Classifiers
We consider the problem of cost sensitive multiclass classification, where we would like to increase the sensitivity of an important class at the expense of a less important one.
Classification in asymmetric spaces via sample compression
We initiate the rigorous study of classification in quasi-metric spaces.
Learning convex polyhedra with margin
We present an improved algorithm for {\em quasi-properly} learning convex polyhedra in the realizable PAC setting from data with a margin.
Nearly optimal classification for semimetrics
We initiate the rigorous study of classification in semimetric spaces, which are point sets with a distance function that is non-negative and symmetric, but need not satisfy the triangle inequality.
Near-optimal sample compression for nearest neighbors
We present the first sample compression algorithm for nearest neighbors with non-trivial performance guarantees.
Efficient Classification for Metric Data
We design a new algorithm for classification in general metric spaces, whose runtime and accuracy depend on the doubling dimension of the data points, and can thus achieve superior classification performance in many common scenarios.
Adaptive Metric Dimensionality Reduction
We study adaptive data-dependent dimensionality reduction in the context of supervised learning in general metric spaces.
Efficient Regression in Metric Spaces via Approximate Lipschitz Extension
We present a framework for performing efficient regression in general metric spaces.
