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Analysis of Perceptron-Based Active Learning
(2005-11-17)
We start by showing that in an active learning setting, the Perceptron algorithm needs $\Omega(\frac{1}{\epsilon^2})$ labels to learn linear separators within generalization error $\epsilon$. We then present a simple ...
Conditional Random People: Tracking Humans with CRFs and Grid Filters
(2005-12-01)
We describe a state-space tracking approach based on a Conditional Random Field(CRF) model, where the observation potentials are \emph{learned} from data. Wefind functions that embed both state and observation into a space ...
Online Learning of Non-stationary Sequences
(2005-11-17)
We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. We derive upper and lower relative loss bounds for a class of universal learning algorithms involving ...
New LSH-based Algorithm for Approximate Nearest Neighbor
(2005-11-04)
We present an algorithm for c-approximate nearest neighbor problem in a d-dimensional Euclidean space, achieving query time ofO(dn^{1/c^2+o(1)}) and space O(dn + n^{1+1/c^2+o(1)}).
An Overview of MOOS-IvP and a Brief Users Guide to the IvP Helm Autonomy Software
(2009-06-18)
This document describes the IvP Helm - an Open Source behavior-based autonomy application for unmanned vehicles. IvP is short for interval programming - a technique for representing and solving multi-objective optimizations ...