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Classification methods has become increasingly popular for biomedical and bioinformatical data analysis. However, due to the difficulty of data acquisition, sometimes we could only obtain small-scale datasets which may leads to unreasonable generalization performances. For SVM-like algorithms, we could resort to Large Margin theory to find out solutions for such dilemma. Recent studies on large margin...
Particle swarm optimisation has been successfully applied as a neural network training algorithm before, often outperforming traditional gradient-based approaches. However, recent studies have shown that particle swarm optimisation does not scale very well, and performs poorly on high-dimensional neural network architectures. This paper hypothesises that hidden layer saturation is a significant factor...
In this paper, a faster supervised algorithm (BPfast) for the neural network training is proposed that maximizes the derivative of sigmoid activation function during back-propagation (BP) training. BP adjusts the weights of neural network with minimizing an error function. Due to the presence of derivative information in the weight update rule, BP goes to `premature saturation' that slows down the...
By considering the geometric properties of the Support Vector Machine (SVM) and Minimal Enclosing Ball (MEB) optimization problems, we show that upper and lower bounds on the radius-margin ratio of an SVM can be efficiently computed at any point during training. We use these bounds to accelerate radius-margin parameter selection by terminating training routines as early as possible, while still obtaining...
This paper describes how to make use of the cost information related to the extraction of each feature in a feature selection algorithm. For instance, in medical diagnosis, the different tests a patient might take during the diagnosis process can have different associated costs. The main idea is to change the feature selection framework in order to get low-cost subsets of informative features. This...
Le but de cette étude est de déterminer l’effet de huit semaines d’entraînement intense en endurance sur l’expression des récepteurs β1- et β2-adrénergiques cardiaques chez des rats diabétiques et contrôles.L’expression du récepteur β1-adrénergique des rats diabétiques sédentaires est significativement diminuée (p<0,05) alors que l’expression de leur récepteur β2-adrénergique n’est pas significativement...
In classification, the class imbalance issue normally causes the learning algorithm to be dominated by the majority classes and the features of the minority classes are sometimes ignored. This will indirectly affect how human visualise the data. Therefore, special care is needed to take care of the learning algorithm in order to enhance the accuracy for the minority classes. In this study, the use...
Most of discoveries indicate that the best way to overcome diabetes is to prevent the risks of diabetes before becoming a diabetic. With this opinion, we would like to find a way to estimate diabetes risk, according to some variables such as age, total cholesterol, gender or shape of the body. Due to having fuzzy input and output (glucose rate) values and because of that dependent variable have more...
It is often that the learned neural networks end with different decision boundaries under the variations of training data, learning algorithms, architectures, and initial random weights. Such variations are helpful in designing neural network ensembles, but are harmful for making unstable performances, i.e., large variances among different learnings. This paper discusses how to reduce such variances...
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