We Use A Gaussian Function As A Kernel Function

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

Based on Chapter 2, Neural Network Method (NN) will be chosen for voice-based command recognition method because it can handle bigger databased. For Neural Network to implement pattern recognition is quite common, and beneficial to use is backpropagation. Supervised learning that starts by inputting the training data through the network is a form of this method. When the data is put in the network, it will generate propagation output activations and then propagated backwards through the neural network, and generating a delta value for all hidden and output neuron. The weights of the network are then update by calculated delta values that generate by neural network, which increase the speech and quality of the learning process.

Training an artificial Neural Network involves choosing and allowing models for models which there are several associated algorithms.

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

RBF is relatively adaptive in nature, which is used for classification and pattern reorganization. The performance of the RBF neural network is compared to the commonly used multilayer network model and the classical logistic regression.RBF neural network is based on the supervised learning. This method works independently and based on many researches and is popular alternative to the traditional MLP (Multi Layer Preceptor).RBF neural networks works perfectly in modeling the non linear data and it can be trained in one stage rather than the iterative process as like

Normally, the automatic segmentation problem is very challenging and it is yet to be fully and satisfactorily solved. The aim of this tumor detection approach is to identify and segment the MRI tumor automatically. It takes into account the statistical features of the brain structure to represent it by significant feature points. Most of the early methods presented for tumor detection and segmentation may be broadly divided into three categories: region-based, edge-based and fusion of region and edge-based methods. Well known and widely used segmentation techniques are K-Means clustering algorithm, supervised method based on neural network classifier [4]. Also, the time spent to segment the tumor is getting reduced due to the detailed representation of the medical image by extraction of feature points. Region-based techniques look for the regions satisfying a given homogeneity criteria and edge based segmentation techniques look for edges between regions with different characteristics

A neural network’s accuracy depends on the data provided to the network. In this case, in order

We will refer to this grid as the input grid. Additionally, a SOM has a second grid, which, for this project, contains thirty-six randomized nodes close to the range of the data points. For us, each of the values is between zero and one. Each of these nodes is six dimensional in order to match our data points, so that now the thirty-six data points are located within the data space. We will refer to this grid as the map grid. Both of these sets of nodes are located in two separate matrixes. The two dimensional node and the six dimensional node located in the same row of the matrixes represent the same point. These randomized points relate to the ones in the static grid one-to-one, allowing us to keep the points in the randomized grid connected to one another in the same pattern that the nodes are connected in the static grid.

The support vector machine classifier creates a hyper plane or multiple hyper planes in high dimensional space that is useful for classification, regression and other efficient task. SVM have a many attractive features due to this gaining popularity and have promising empirical performance. SVM build hyper plane in original input space to separate the data points. Some time that is difficult to perform separation of data points in original input space, so to make separation easier the original finite dimensional space mapped into new higher dimensional space. SVM is works on principal that data points are classified using a hyper plane which maximizing the separation between data points and the hyper plane is constructed with the help of support vectors

Each learing or training methods in supervised learing depends on the idea display data training in front of the network in the form of a pair of forms input form and target form. (See Figure (2-5)) .

CNN. This will include a literature review of the most recent peer-reviewed papers in computer

In the second research paper, researchers focus on a varia¬tion of the standard back propagation algorithm. Since nodes of each layer are fully connected in the standard back propagation neural networks, huge computing resour¬ces are cost to adjust weights. Therefore researchers pro¬pose an exclusive connecting network.

In the machine learning, one of the simplest method for classification is the K-nearest neighbor algorithm. Given a new observation x∈R, K training observations from the rows of the Xtm closest in distance to x are found. After that, using the mainstream division among these K nearest observations from the training set, x is classified. Consequently, the performance of KNN algorithm depends on the choice of K and the algorithm is sensitive to local structure of

In this chapter, we will discuss our experimental results. The aim of this experiment to find more accurate results for remote sensing image scene classification. In this experiment we have used NWPU-RESISC45 dataset. We applied some normal learning procedure and deep learning procedure in this experiment. In deep learning procedure we used two well-known deep learning named VGG16 and ResNet50. We, trained them from scratch as well as applied transfer learning on our dataset. We discussed about the procedures in the previous chapter. We will discuss the results, make comparison and eventually draw a conclusion on this chapter. We will also discuss about implementation environment and dataset.

As a result, there will be a lot of redundant computation as the pixels in nearby chunks overlap. FCN [15], Unet [20] and SegNet [1] applied similar frameworks to solve these image segmentation problems. FCN(Fully Convolution Networks) used traditional classification neural networks initially to track the features with deep, coarse, semantic data from the image, and then combined it with shallow, fine, appearance information in the last several layers of the network. The first part of the FCN determines the content in the image, while the second part combines with the location information of the objects to get the segment the input image. Unet refined the FCN architecture further.