178 research outputs found
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Embodied learning for visual recognition
The field of visual recognition in recent years has come to rely on large expensively curated and manually labeled "bags of disembodied images". In the wake of this, my focus has been on understanding and exploiting alternate "free" sources of supervision available to visual learning agents that are situated within real environments. For example, even simply moving from orderless image collections to continuous visual observations offers opportunities to understand the dynamics and other physical properties of the visual world. Further, embodied agents may have the abilities to move around their environment and/or effect changes within it, in which case these abilities offer new means to acquire useful supervision. In this dissertation, I present my work along this and related directions.Electrical and Computer Engineerin
A brief history of learning classifier systems: from CS-1 to XCS and its variants
© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
A novel non-intrusive objective method to predict voice quality of service in LTE networks.
This research aimed to introduce a novel approach for non-intrusive objective
measurement of voice Quality of Service (QoS) in LTE networks. While achieving this aim, the thesis established a thorough knowledge of how voice traffic is
handled in LTE networks, the LTE network architecture and its similarities and
differences to its predecessors and traditional ground IP networks and most
importantly those QoS affecting parameters which are exclusive to LTE environments. Mean Opinion Score (MOS) is the scoring system used to measure
the QoS of voice traffic which can be measured subjectively (as originally intended). Subjective QoS measurement methods are costly and time-consuming,
therefore, objective methods such as Perceptual Evaluation of Speech Quality
(PESQ) were developed to address these limitations. These objective methods
have a high correlation with subjective MOS scores. However, they either require individual calculation of many network parameters or have an intrusive
nature that requires access to both the reference signal and the degraded signal
for comparison by software. Therefore, the current objective methods are not
suitable for application in real-time measurement and prediction scenarios.
A major contribution of the research was identifying LTE-specific QoS affecting parameters. There is no previous work that combines these parameters to
assess their impacts on QoS.
The experiment was configured in a hardware in the loop environment. This
configuration could serve as a platform for future research which requires simulation of voice traffic in LTE environments.
The key contribution of this research is a novel non-intrusive objective method
for QoS measurement and prediction using neural networks. A comparative
analysis is presented that examines the performance of four neural network
algorithms for non-intrusive measurement and prediction of voice quality over
LTE networks. In conclusion, the Bayesian Regularization algorithm with 4 neurons in the hidden layer and sigmoid symmetric transfer function was identified as the best solution with a Mean Square Error (MSE) rate of 0.001 and
regression value of 0.998 measured for the testing data set
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