Learning with Fredholm Kernels

no code implementations • NeurIPS 2014 • Qichao Que, Mikhail Belkin, Yusu Wang

In this paper we propose a framework for supervised and semi-supervised learning based on reformulating the learning problem as a regularized Fredholm integral equation.

Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval

no code implementations • CVPR 2015 • Ke Jiang, Qichao Que, Brian Kulis

We present a simple but powerful reinterpretation of kernelized locality-sensitive hashing (KLSH), a general and popular method developed in the vision community for performing approximate nearest-neighbor searches in an arbitrary reproducing kernel Hilbert space (RKHS).

Image Retrieval Retrieval

Inverse Density as an Inverse Problem: The Fredholm Equation Approach

no code implementations • NeurIPS 2013 • Qichao Que, Mikhail Belkin

In this paper we address the problem of estimating the ratio $\frac{q}{p}$ where $p$ is a density function and $q$ is another density, or, more generally an arbitrary function.

Transfer Learning