4,457 research outputs found
Explaining computer predictions with augmented appraisal degrees
An augmented appraisal degree (AAD) has been conceived as a mathematical representation of the connotative meaning in an experience-based evaluation, which depends on a particular experience or knowledge. Aiming to improve the interpretability of computer predictions, we explore the use of AADs to represent evaluations that are per- formed by a machine to predict the class of a particular object. Hence, we propose a novel method whereby predictions made using a support vector machine classification process are augmented through AADs. An illustra- tive example, in which the classes of handwritten digits are predicted, shows how the augmentation of such predictions can favor their interpretability
Fast and robust learning by reinforcement signals: explorations in the insect brain
We propose a model for pattern recognition in the insect brain. Departing from a well-known body of knowledge about the insect brain, we investigate which of the potentially present features may be useful to learn input patterns rapidly and in a stable manner. The plasticity underlying pattern recognition is situated in the insect mushroom bodies and requires an error signal to associate the stimulus with a proper response. As a proof of concept, we used our model insect brain to classify the well-known MNIST database of handwritten digits, a popular benchmark for classifiers. We show that the structural organization of the insect brain appears to be suitable for both fast learning of new stimuli and reasonable performance in stationary conditions. Furthermore, it is extremely robust to damage to the brain structures involved in sensory processing. Finally, we suggest that spatiotemporal dynamics can improve the level of confidence in a classification decision. The proposed approach allows testing the effect of hypothesized mechanisms rather than speculating on their benefit for system performance or confidence in its responses
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm
In recent days, Artificial Neural Network (ANN) can be applied to a vast
majority of fields including business, medicine, engineering, etc. The most
popular areas where ANN is employed nowadays are pattern and sequence
recognition, novelty detection, character recognition, regression analysis,
speech recognition, image compression, stock market prediction, Electronic
nose, security, loan applications, data processing, robotics, and control. The
benefits associated with its broad applications leads to increasing popularity
of ANN in the era of 21st Century. ANN confers many benefits such as organic
learning, nonlinear data processing, fault tolerance, and self-repairing
compared to other conventional approaches. The primary objective of this paper
is to analyze the influence of the hidden layers of a neural network over the
overall performance of the network. To demonstrate this influence, we applied
neural network with different layers on the MNIST dataset. Also, another goal
is to observe the variations of accuracies of ANN for different numbers of
hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
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