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An efficient algorithm for the calculation of the approximate Hessian matrix for the Levenberg-Marquardt (LM) optimization algorithm for training a single-hidden-layer feedforward network with linear outputs is presented. The algorithm avoids explicit calculation of the Jacobian matrix and computes the gradient vector and approximate Hessian matrix directly. It requires approximately 1/N the floating...
The task of classifying data has been addressed in various works, and has been utilized in various areas of application, such as medicine, industry, marketing, financial market and many others. This work will present a data classifier proposal that combines the SOM (Self-Organizing Map) neural network with INN (Informative Nearest Neighbors). The combination of these two algorithms will be called...
We present a complementary ensemble selection method that utilizes a novel priority queue-based diversity measure. The method considers voting weaknesses of the current ensemble in covering the training set, and finds a classifier that can remove the highest priority weaknesses. Individual classifiers are generated using different machine learning algorithms and different parameter settings.
Inflammatory bowel disease (IBD) is a group of inflammatory diseases of the human colon and small intestine. IBD symptoms are non-specific; diagnosis can be delayed because an invasive colonoscopy is required for confirmation. Delayed diagnosis is linked to poor growth in children. Imbalances in the human intestinal microbiome - the community of microorganisms that reside in the human gut - are thought...
Inspired on decision trees and evolutionary algorithms, this paper proposes a learning algorithm of constructive neural networks that relies on three principles: to layout the neurons in a tree-like structure; to train each neuron individually; and, to optimize all the weights using an evolutionary approach. This way, it is expected to advance in two main questions concerning multilayer perceptrons...
Metric learning has been successful in distance based classification tasks. However, metric learning tends to become increasingly complex with the increase of input feature dimensionality. Therefore, application of an efficient feature extraction and dimensionality reduction technique prior to metric learning has been pursued. Conventional feature extraction and dimensionality reduction techniques...
Gaussian mixture models (GMM) remain popular in pattern classification applications due to their well understood Bayesian framework and the availability of good training algorithms such as the expectation maximization (EM) algorithm. EM is a non-discriminative training algorithm. The performance of a GMM trained with the EM algorithm can often fall short of other discriminative pattern classification...
We review the fact that several kinds of neural networks can be trained to approximate other types of discriminant functions, thereby throwing some doubt upon the utility of the No Free Lunch theorem. Using a license plate recognition database with 36 classes, we then demonstrate that multilayer perceptrons estimate posterior probabilities very poorly when the number of classes is large. A method...
In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed. Multi-label classification is one of the actively researched machine learning paradigm that has gained much attention in the recent years due to its rapidly increasing real world applications. In contrast to traditional binary and multi-class classification, multi-label classification...
Machine learning (ML) based applications that require data stream processing have become quite common over the past few years. To deal with continuous and massive streams of data, low computational and memory costs are required from the ML techniques employed; these requirements can be partially fulfilled by using constructive neural networks (CoNN) algorithms. The automatic definition of the Neural...
Multi-label learning is popular in current research of machine learning areas, and there have already been many methods using label relationship to solve multi-label problems. However, the meaning of their relationship is not so obvious that it's hard for us to know the fact among labels. Besides, with the development of multi-label learning, hierarchical multi-label classification is a new research...
Learning from crowds, which the labels of the instances are collected through crowdsourcing ways, has become an important research topic recently. Personal Classifier (PC) approach is a representative approach for learning from crowds due to its convex optimization formulation. PC approach makes assumptions about parameters' distribution, thus it is a parametric approach. However, these assumptions...
Reliable fault diagnosis and potential remaining useful life (RUL) predication before the occurrence of fatal failure in machinery is critical for improving productivity and reducing maintenance cost. However, the existing physics heuristics and neural networks based methods face difficulties to treat such two issues simultaneously. This paper proposes a novel Network of Extreme Learning Machines...
In this paper, we conduct a preliminary study about a newly established course evaluation survey given to the students by our institution. This survey contains several free-text questions which generates more qualitative but also voluminous unstructured feedback compared to the previous Likert scale question-based survey. Our aim is to apply data mining techniques to extract knowledge from these surveys,...
In order to reduce the computational complexity of kernel machines and improve their performance in multi-label classification, we develop a systematic two step batch approach for constructing and training a new multiclass kernel machine (MKM). The proposed paradigm prunes the kernels, and uses Newton's method to improve the kernel parameters. In each iteration, output weights are found using orthogonal...
Internet traffic classification is one of the key foundations for research works and traffic engineering in Internet. With the rapid increase of Internet applications and the number of Internet flow, the technique challenges are coupled with development of traffic classification all the time. Currently, the machine learning-based technique has attracted much attention, since it can address the issues...
One identical weighting scheme for each sample of one cluster is often employed in the traditional sample weighting k-means clustering. However, this paper proposes a novel sample weighting k-means clustering algorithm based on angles information(SWKMA). In this presented SWKMA, firstly, samples of one cluster is divided into two types according to angles information, and secondly, different weighting...
Support vector machines (SVM), originally introduced as powerful binary classifiers, can also be used for multi-class recognition with the help of creative meta-learning strategies such as commonly used one-vs-rest, one-vs-one and majority voting. In this paper, we explore the potential of creating informed nested dichotomies based on clustering pseudo-labels and probability estimates generated a...
Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models...
Wireless capsule endoscopy (WCE) plays a significant role in the non-invasive small intestine screening for obscure gastrointestinal bleeding detection. However, the task of reviewing 60,000 frames to detect the bleeding encumbers the clinician, leading to visual fatigue and false diagnosis. In this paper, we propose a color feature based bleeding detection system with feature selection using a modified...
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