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Stock market is a complex non-linear dynamic system which is affected by many factors. Traditional analysis and forecasting methods are insufficient to accurately reveal the inherent pattern of the stock market, resulting in a big difference between expected and observed results. In recent years, machine learning analytical methods are applied to the stock selection model more often than before and...
Skin detection is one of the most important targets of image processing and computer vision. One big concern about skin detection algorithms is their simplicity while keeping a good accuracy in discriminating skin and non-skin pixels. This paper presents a novel and robust skin detector. In this study, statistical information of each pixel and its neighbours were taken into account in order to deal...
Since Krizhevsky won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 competition with the brilliant deep convolutional neural networks(D-CNNs), researchers have designed lots of D-CNNs. However, almost all the existing very deep convolutional neural networks are trained on the giant ImageNet datasets. Small datasets like CIFAR-10 has rarely taken advantage of the power of depth...
Because of the various appearance (different writers, writing styles, noise, etc.), the handwritten character recognition is one of the most challenging task in pattern recognition. Through decades of research, the traditional method has reached its limit while the emergence of deep learning provides a new way to break this limit. In this paper, a CNN-based handwritten character recognition framework...
In performing data mining, a common task is to search for the most appropriate algorithm(s) to retrieve important information from data. With an increasing number of available data mining techniques, it may be impractical to experiment with many techniques on a specific dataset of interest to find the best algorithm(s). In this paper, we demonstrate the suitability of tree-based multi-variable linear...
We propose a novel type of maxout that uses filters with kernels of multiple sizes for generating convolved maps. These filters extract the most effective features for recognition from many different variations of texture patterns. A convolved map is generated by convolution between feature maps and filters. If the size of filters is varied, the size of the convolved map will also vary; in which case,...
Rule based classification or rule induction (RI) in data mining is an approach that normally generates classifiers containing simple yet effective rules. Most RI algorithms suffer from few drawbacks mainly related to rule pruning and rules sharing training data instances. In response to the above two issues, a new dynamic rule induction (DRI) method is proposed that utilises two thresholds to minimise...
Most of the object contour detection approaches suffers from some drawbacks such as noise, occlusion of objects, shifting, scaling and rotation of objects in image which they are failed to recognize object contour. A solution to solve the problem is utilization of feature extractor which is independent of rotation, scaling, transformation and etc. One of the best options for the task of feature extraction...
The ability of generalization by random forests is higher than that by other multi-class classifiers because of the effect of bagging and feature selection. Since random forests based on ensemble learning requires a lot of decision trees to obtain high performance, it is not suitable for implementing the algorithm on the small-scale hardware such as embedded system. In this paper, we propose a boosted...
Automatic handwriting recognition of digit strings, is of academic and commercial interest. Current algorithms are already quite good at learning to recognize handwritten digits, which enables to use them for sorting letters and reading personal checks. Neural networks are a powerful technology for classification of visual inputs arising from documents, and have been extensively used in many fields...
Determination of model complexity is a challenging issue to solve computer vision problems using restricted boltzmann machines (RBMs). Many algorithms for feature learning depend on cross-validation or empirical methods to optimize the number of features. In this work, we propose an learning algorithm to find the optimal model complexity for the RBMs by incrementing the hidden layer. The proposed...
This paper presents a novel technique for modeling of photovoltaic (PV) array using random forests (RFs). Metrological variables such as solar radiation and ambient temperature as well as actual output current of a 3 kWp PV grid-connected system installed at Universiti Kebangsaan Malaysia have been utilized. These data are used to train and validate the proposed RFs model. Three statistical error...
In the last years, numerous investigations have been made within the field of faults diagnosis in induction motors. Most of them use data obtained either from the time domain, through advanced techniques in the frequency domain or even by simulation tools. Some researchers have employed a considerable effort in designing sophisticated algorithms to achieve the best performance of the diagnosis system...
Different to other re-sampling ensemble learning, negative correlation learning trains all individual models in an ensemble simultaneously and cooperatively. In negative correlation learning, each individual could see all training data, and adapt its target function based on what the rest of individuals in the ensemble have learned. In this paper, two error bounds are introduced in negative correlation...
Among the classification algorithms in machine learning, the KNN (K nearest neighbor) algorithm is one of the most frequent used methods for its characteristics of simplicity and efficiency. Even though KNN algorithm is very effective in many situations while it still has two shortcomings, not only is the efficiency of this classification algorithm obviously affected by redundant dimensional features,...
C5.0 classifier is optimized using Bayesian theory. The C5.0 algorithm is a classifier that discovers patterns in data and uses them to make accurate predictions. It classifies data objects based on the information gain of its attributes. Though it responds to noisy and missing data, its accuracy can be improved upon. This research work proposes a post pruning decision tree algorithm that will use...
Two error bounds were introduced in the learning process of balanced ensemble learning. They are the lower bound of error rate (LBER) and the upper bound of error output (UBEO) on the training set, respectively. These two error bounds would decide whether a training data point should be further learned or not after balanced ensemble learning has reached certain stage. Before the error rates are higher...
We introduce a novel adaptive neuro-fuzzy architecture based on the framework of Multiple Instance Fuzzy Inference. The new architecture called Multiple Instance-ANFIS (MI-ANFIS), is an extension of the standard Adaptive Neuro Fuzzy Inference System (ANFIS) [1] that is designed to handle reasoning with multiple instances (bags of instances) as input and capable of learning from ambiguously labeled...
In this paper, we assume that we have two types of datasets for classifier design. One is an in-house dataset which is fully available for classifier design as training data. The other is an external dataset which is kept under a very severe privacy preserving policy. We assume that the available information on the external dataset is only the error rate of a presented classifier. No other information...
The paper presents a system to accurately differentiate between unique individuals by utilizing the various eye-movement biometric features. Eye Movements are highly resistant to forgery as the generation of eye movements occur due to the involvement of complex neurological interactions and extra ocular muscle properties. We have employed Linear Multiclass SVM model to classify the numerous eye movement...
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