A Hybrid Fuzzy Approach for Human Eye Gaze Pattern Recognition
in the respective visual patterns make them differ from each other is very important for us to investigate the correlation
between human’s visual behavior and cognitive processes. We introduce our fuzzy signatures with a Levenberg-Marquardt optimization
method based hybrid approach for recognizing the different eye gaze patterns when a human is viewing faces or text documents.
Our experimental results show the effectiveness of using this method for the real world case. A further comparison with Support
Vector Machines (SVM) also demonstrates that by defining the classification process in a similar way to SVM, our hybrid approach
is able to provide a comparable performance but with a more interpretable form of the learned structure.
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Conference Paper: A Hybrid Fuzzy Approach for Human Eye Gaze Pattern Recognition
- SourceAvailable from: André Frank Krause
[Show abstract] [Hide abstract] ABSTRACT: While the No-Prop (no back propagation) algorithm uses the delta rule to train the output layer of a feed-forward network, No-Prop-fast employs fast linear regression learning using the Hopf-Wiener solution. Ten times faster learning speeds can be achieved on large datasets like the MNIST benchmark, compared to one of the fastest back-propagation algorithm known. Additionally, the plain feed-forward network No-prop-fast can distinguish gaze movements on cartoons with and without text, as well as age-specific attention shifts between text and picture areas with minimal preprocessing. Continuously learning mobile robots and adaptive intelligent systems require such fast learning algorithms. Almost real-time learning speeds enable lower turn-around cycles in product development and data analysis.
- "ta was pre-processed by several signal processing routines, resulting in 80 spectral power density values serving as inputs to a LVQ net.  applied neural networks to validate if the way a person reads influences the way he understands information and proposed a novel method of detecting the level of engagement in reading based on gaze-patterns.  applied their hybrid fuzzy signatures with Levenberg-Marquardt optimization method for recognizing different eye-gaze patterns while viewing faces or text documents. All these approaches have in common that the gaze data needs a high amount of pre-processing before it can serve as input to the neural networks. In a previous study, we sh"
- "It has been more emphasized heavily in engineering science recently. For example, FCMs are applied in e-commerce intelligent prediction , credibility factor assessment , medical assistance decision support system , information security  and so on. FCMs show high efficiency and accuracy in these systems. "
Conference Paper: Fuzzy Rough Signatures[Show abstract] [Hide abstract] ABSTRACT: We extend the idea of Fuzzy Signature to Fuzzy Rough Signature (FRS). The proposed Fuzzy Rough Signature is capable of handling most kind of uncertainty: epistemic and random uncertainty, vagueness due to indiscernibility, and linguistic vagueness that exists in both large as well as small sample data sets. Additionally, this system is capable of hierarchical organization of inputs and use of flexible aggregation selection will simplify the combinations of inputs from different sources.