2 research outputs found
Multi-objective decision in machine learning.
Thiswork presents a novel approach for decisionmaking
for multi-objective binary classification problems.
The purpose of the decision process is to select within a set of
Pareto-optimal solutions, one model that minimizes the structural
risk (generalization error). This new approach utilizes
a kind of prior knowledge that, if available, allows the selection
of a model that better represents the problem in question.
Prior knowledge about the imprecisions of the collected data
enables the identification of the region of equivalent solutions
within the set of Pareto-optimal solutions. Results for
binary classification problems with sets of synthetic and real
data indicate equal or better performance in terms of decision
efficiency compared to similar approaches
Combined weightless neural network FPGA architecture for deforestation surveillance and visual navigation of UAVs.
This work presents a combined weightless neural network architecture for deforestation surveillance and visual
navigation of Unmanned Aerial Vehicles (UAVs). Binary images, which are required for position estimation and
UAV navigation, are provided by the deforestation surveillance circuit. Learned models are evaluated in a real
UAV flight over a green countryside area, while deforestation surveillance is assessed with an Amazon forest
benchmarking image data. Small utilization percentage of Field Programmable Gate Arrays (FPGAs) allows for
a higher degree of parallelization and block processing of larger regions of input images