Voting Based Extreme Learning Machine Essay examples

imaging application. This is because the crucial data required for the classification phase are derived at this stage. Feature extraction is the process of estimating

The objective of the neural network is to transform the input to meaningful output. Neural networks are often used for statistical analysis and data modeling. Neural network has many uses in data processing, robotics, and medical diagnosis [2]. From the starting of the neural network there are various types found, but each and every types has some advantages and disadvantages. Deep learning and -neural network software are the categories of artificial neural network. The parallel process also allows ANNs to process the large amount of data very efficiently. The artificial neural network is built with a systematic

It is the best method to achieve representative sample in which sample has an acknowledge chance

[Key words] prediction, multicollinearity, high dimension, principle component analysis, robust regression, ridge regression, linear regression

Machine learning and deep neural networks are quickly finding themselves in everyday consumer products and services, and even enterprise applications. Some of their uses range from facial recognition in photo albums to object recognition in self-driving cars. Although classifying people in a photo album poses no real safety concerns, an inaccurate classifier in a self-driving car can have disastrous effects. As a technology with any bearing on security undergoes wide adoption, breaching said security measure becomes lucrative to attackers. As machine learning is implemented in security systems using facial recognition, malware protection software, bot identifying

been applied was used by van Diest et al.[10]. The usage of Support Vector Ma-

In order to have a better understanding of the mechanism used in the prediction process, we need to know what are the stopping conditions, as this process involves a finite number of iterations known a priori. It has been previously proved one of the problems that occur during the training of a neural networks is overfitting towards the input data, which generates low quality predictions [56].

In medical fields, clinical databases are recently becoming increasingly large so that more experts are needed to deal the data. Since the majority of the medical data sets are non-linear, it is difficult for people to analysis and classify a large amount of them. Therefore if there are some specific algorithms which could automatically handle numerous data, it could efficiently reduce the human

for us, our goal is to accurately estimate the accuracy of a given trained classifier

The classification of the data is the problem to observe new numbers of discrete categories. The aim is to learn a model making accurate predictions on new observations based on a set of data points. For example, for the observations set, x1, …, xn, the corresponding categories, y1, …, yn, where yi ∈{-1, +1}. Here, yi=-1 refers to category of failure region, and yi=+1 refers to safe region. A new observation, x value, is assigned to one of these categories.

A widely used strategy[26, 4] is to face this problem as a binary classification task, with

Attribute reduction is a challenge in that is has a big eect on classification accuracy as the classifier training time, complexity

ellaHassanien, A., &Ślzak, D. (2006). Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer.International Journal of Hybrid Intelligent Systems, 3(4), 205-218.‏

The proposed WLEM is evaluated using the two weighting schemes which assigns classwise weights to the instances. The performance of WELM is dependent on the choice of these weights. The weighing schemes proposed only considers class skewness for assigning weights to the instances. It ignores other data complexity measures as class overlapping, shape of the class, length of decision boundary, class distribution etc. for computing the weights associated with the instances. Assigning weights to the instances is a difficult task as we don't know that the factors responsible for data complexity is different for different datasets.

To construct an optimal hyperplane, SVM employs an iterative training algorithm, which is used to minimize an error function. According to the form of the error function, SVM models can be classified into four distinct groups: