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This paper argues that spiking neural networks (SNN) are effective techniques for object recognition and introduces for the first time a SNN model for obstacle recognition to assist blind people wearing prosthetic vision devices by modelling and classifying spatio-temporal (ST) video data.
Model-based approaches have been for long an effective method to model data and classify it.
The corresponding numbers for manual analysis are 145 isoforms (26%) modelled and 48% classified as structurally consistent.
In this paper, we learn local features from a voxelized representation of a CAD model and classify its manufacturability.
We used a morphological shared-weight neural network (MSNN) to learn an implicit vehicle model and classify pixels into vehicles and non-vehicles.
We use the data collected from loop detectors on freeway to establish traffic flow model and classify the flow into three states based on K-means method.
This work includes a formal description of a method that, from that initial information, combines model slicing and constraint analysis techniques to create the client domain model, and classifies the constraints according to their server independency.
Topper will use machine learning to build a model and classify all 100 results.
(3) The traditional method of object detection based on feature matching can only detect and recognize one type object by a given template, but cannot model and classify multi type objects at the same time. .
The evaluation process used 6000 images from six different mobile phones of the different models and classified them using Apache Mahout, a scalable machine learning tool which runs on Hadoop.
In this paper, we build an economic model of a non-linear system of difference equations and present a qualitative study for the obtained model, where a mathematical model of a bounded rationality multiple game with an exponential demand function will be introduced, and then we obtain the equilibrium points of the model and classify if they are locally stable or not.
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