Disadvantages Of Support Vector Machine

The big data analytics deals with a large amount of data to work with and also the processing techniques to handle and manage large number of records with many attributes. The combination of big data and computing power with statistical analysis allows the designers to explore new behavioral data throughout the day at various websites. It represents a database that can’t be processed and managed by current data mining techniques due to large size and complexity of data. Big data analytic includes the representation of data in a suitable form and make use of data mining to extract useful information from these large dataset or stream of data. As stated above the big data analytics has recently emerged as a very popular research and practical-oriented framework that implements i) data mining, ii) predictive analysis forecasting, iii) text mining, iv) virtualization, v) optimization, vi) data security, vii) virtualization tools for processing very large data sets. In the implementation of big data applications, new data mining techniques and virtualization are required to be implemented due to the volume, variability, forms and velocity of the data to be processed. A set of machine learning techniques based on statistical analysis and neural networking technology for big data is still evolving but it shows a great potential for solving a big data business problems. Further, a new concept of in-memory database for enhancing the speed for analytic processing is further helping

Purpose: The electromyography (EMG) signal is a bioelectrical signal variation, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. The EMG signal is a complicated biomedical signal due to anatomical/ physiological properties of the muscles and its noisy environment. Support Vector Machines (SVMs) is an extensively used machine learning method with many biomedical signal classification applications. In this article, Discrete Wavelet Transform (DWT) has been applied for EMG noise removing and feature extraction, then Elephant Herding Optimization (EHO) and Water Wave Optimization (WWO) were used to find a subset of EMG feature

In the second scenario Iris dataset, which is one of the most common standard datasets is used. It consists of four attributes, 150 training samples, 150 testing samples, three classes, and three outputs as shown in Tables (\ref{Table:DatasetDescription}). The results of this dataset are summarized in Table (\ref{Table:Results}).

AdaBoost or Adaptive Boosting was a method proposed by Freund and Schapire [15]. It boosts the performance by fitting a sequence of weak learners on repeatedly modified versions of data.

Granular parakeratosis is a skin disease that is identified by brownish-red keratotic papules that can coalesce into plaques. This is a rare disorder of keratinization with a distinctive histology wherein parakeratosis with retention of keratohyaline granules is identified in the epidermis. An effective image processing system support vector machine can be potentially used to segment the lesions of granular parakeratosis. Image Segmentation is one of the important issues occurred in times of before computer visualization. The fundamental objective of image segmentation is to segment a picture into its constituent areas. This segmentation can be applied to skin disease like granular parakeratosis and later Support Virtual Machine

Neurodegenerative diseases causes a wide variety of mental symptoms whose evolution is not directly related to the analysis made by radiologists on basis of images, who can hardly quantify systematic differences. This paper presents a new automatic (Based on software program) image analysis method that reveals different brain patterns associated to the presence of neurodegenerative diseases, finding systematic differences and therefore grading objectively any neurological disorder. An accurate solution can be provided by using Alzheimer’s diseases based on saliency map characterization is carried out on database images. This paper gives automatic image analysis method and attempts an approach for classification of brain images to search for pathology and normality part of brain by extracting salient features of input brain image and the region of interest is identified using kernel k-means algorithm. A support vector machine (SVM) a supervised learning process is used for classification of AD, which is recognized on basis of blue color is normal brain part and red color is pathology related.

Initially [4] the images similar to the query image are extracted from a large group of medical images. Then the search is by accelerating the retrieval process with the help of Support Vector Machine (SVM) classifier. The performance of the retrieval system is enhanced by adapting the subjective feedback method of Support Vector Machine (SVM) classifier. The performance of the retrieval system is enhanced by adapting the subjective feedback method.

Using big data analysis, the capability of predictive analytics through machine learning to recognize patterns in open-source data, supports

We experiment with two different tree ensemble methods- gradient boosting (Xgboost) and random forest, and compare their performance together with kernel SVM algorithm. We briefly describe these algorithms below.

This paper focuses on extracting sensitive features to embedding modification , Statistical moments of characteristic functions of image run-length histogram and its variants are taken as features. SVM is utilized as classifier. The first three moments of the CF of three image RLHs are selected as features to distinguish the plain cover image from stego images. This method has a better performance to an untrained stego-algorithm compared to others. Proposed 36-D feature vector provides clearly better detection accuracy compared with 78-D feature vector and the 108-D feature

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

It may be possible to visualise such relationship for two- or three-dimensional problems, yet, a computer algorithm will be needed to optimise the decision boundary in higher-dimensional feature spaces.

The ASM model was proposed by Cootes et al [8]. This was used for feature extraction by characterizing

The objective of this research is utilized ensemble learning method that combines the vote of multiple learner classifiers by using the different subset of feature to improve the accuracy of supervised learning. Another advantage of ensemble methods, it enhances the performance by using different types of classifiers together because it reduces the variance between them and keeps bias error rate without increasing. Two main types of ensemble methods [9]: Bagging and Boosting techniques [10]. The first one depends on subsampling of the training dataset by replacement sample and generates training subset then

In Paper [5] they actualized K-implies alongside Genetic algorithm for dimensionality diminishment and bolster vector machine to group the informational collection. K-implies calculation is utilized to expel exceptions and the loud information. The ideal highlights are chosen by utilizing the hereditary calculation and afterward Support vector machine orders the lessened information space utilizing 10 crease cross approval strategy. Genetic algorithm chooses diverse highlights from unique arrangement of highlight amid each run. To get steady outcomes, the analysis was evaluated fifty times. The consequence shows that the expected replica gains the correctness of 98.82%.