SVM Learning Algorithm Research Articles

Machine Learning Algorithms for Spam Detection in Social Networks

Most of the web based social systems like Face book, twitter, other mailing systems and social networks are developed for users to share their information, to interact and engage with the community. Most of the times these social networks will give some troubles to the users by spam messages, threaten messages, hackers and so on.. Many of the researchers worked on this and gave several approaches to detect the spam, hackers and other trouble shoots. In this paper we are discussing some tools to detect the spam messages in social networks. Here we are using RF, SVM, KNN and MLP machine learning algorithms across rapid miner and WEKA. It gives the better results when compared with other tools.

Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models

Since its introduction, television has been the main channel of investment for advertisements in order to influence customers purchase behavior. Many have attributed the mere exposure effect as the source of influence in purchase intention and purchase decision; however, most of the studies of television advertisement effects are not only outdated, but their sample size is questionable and their environments do not reflect reality. With the advent of the internet, social media and new information technologies, many recent studies focus on the effects of online advertisement, meanwhile the investment in television advertisement still has not declined. In response to this, we applied machine learning algorithms SVM and XGBoost, as well as Logistic Regression, to construct a number of prediction models based on at-home advertisement exposure time and demographic data, examining the predictability of Actual Purchase and Purchase Intention behaviors of 3000 customers across 36 different products during the span of 3 months. If we were able to predict purchase behaviors with models based on exposure time more reliably than with models based on demographic data, the obvious strategy for businesses would be to increase the number of adverts. On the other hand, if models based on exposure time had unreliable predictability in contrast to models based on demographic data, doubts would surface about the effectiveness of the hard investment in television advertising. Based on our results, we found that models based on advert exposure time were consistently low in their predictability in comparison with models based on demographic data only, and with models based on both demographic data and exposure time data. We also found that there was not a statistically significant difference between these last two kinds of models. This suggests that advert exposure time has little to no effect in the short-term in increasing positive actual purchase behavior.

Local dense mixed region cutting + global rebalancing: a method for imbalanced text sentiment classification

The category imbalance of data in text sentiment classification is a widely existent phenomenon, and it is a serious challenge for designing an effective classifier. In this paper, we propose a two-stage data balancing scheme for text sentiment classification, which not only can make the data boundary clear, but also can balance the class distribution of training data set. The core algorithm LDMRC of the scheme is proposed based on the shortest distance from a point to a straight line, to remove some majority class texts in the neighborhood of a minority class text for balancing the class distribution of data in the local dense mixed region. The second stage employs SS or RS as a data rebalancing strategy to globally balance the training dataset after local dense mixed region cutting. The proposed two-stage data balancing scheme is used by situating at the front of a learning algorithm such as SVM. Using the machine learning algorithm SVM on eight imbalanced data sets including Book_c, Hotel, Jadeite, Insurance in Chinese, and DVD, Book_e, Electronics, Kitchen in English, we verify the effectiveness of the proposed method. The experimental results show that LDMRC is superior to the best existing cutting algorithm BRC for Acc, RN and FN. Furthermore, LDMRC+SS and LDMRC+RS are superior to the corresponding method LDMRC on Chinese datasets. This indicates that alone use of local boundary cutting cannot obtain the best effect, and data rebalancing strategies are necessary for text sentiment classification.

Sentiment Analysis for Product Recommendation Using Random Forest

Analysis of sentiments is to analyze the natural language and to find the emotions, express by the human beings. The idea behind sentiment analysis is to determine polarity of textual opinion given by person. Sentiment Analysis is useful in product recommendations. Based on the reviews given by the user; the products can be recommended to another user. Major product websites are using sentiment analysis to understand the popularity and problems with the product. Sentiment analysis mainly formulated as two class classification problem, positive and negative. Sentiment analysis using ordinal classification gives more clear idea about sentiments. The proposed system determines polarity of reviews given by users, using ordinal classification. The system will give polarity using machine learning algorithms SVM and Random Forest. The achieved polarity will be used to provide recommendation to users.

Research on Vocal Tic Symptom Detecting Using SVM/HMM

Vocal tic disorder represents a disease of making weird sounds without any reason. This disease must be found and cured in the early stage of the disease in order to prevent development to Tourette’s syndrome. In this paper, we introduce a machine learning based speech recognition technology to detect vocal tic disorder in the early stage. Vocal tic disorder has repetition but an irregular tic pitch that vary in each patient. To identify vocal tic disorder with these characteristics we used MFCC, a speech feature extraction method, and to generate a recognition model we used machine learning algorithms SVM and HMM. We confirmed an 88% recognition in the SVM algorithm and a 62% recognition in the HMM algorithm. This confirms that in identifying vocal tic disorders with repetition but irregular tic pitches SVM has a better recognition rate than HMM.

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Abstract This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.

계층적 특징 학습을 이용한 3차원 물체 인식 시스템의 설계

In this paper, we propose an object recognition system that can effectively find out its category, its instance name, and several attributes from the color and depth images of an object with hierarchical feature learning. In the preprocessing stage, our system transforms the depth images of the object into the surface normal vectors, which can represent the shape information of the object more precisely. In the feature learning stage, it extracts a set of patch features and image features from a pair of the color image and the surface normal vector through two-layered learning. And then the system trains a set of independent classification models with a set of labeled feature vectors and the SVM learning algorithm. Through experiments with UW RGB-D Object Dataset, we verify the performance of the proposed object recognition system.

Research and Implementation of View Block Partition Method for Theme-oriented Webpage

A semantic block is treated as a unit while analyzing the webpage. First, we implement the VTPS algorithm to partition a webpage into semantic blocks. Then, we propose an algorithm to extract the spatial and content features, and then construct the feature vector for each block. Based on these vectors, the SVM learning algorithm is applied to train and classify the various theme-oriented webpage blocks. At last, the classification experiments show the efficiency of this method.

A New SVM Active Learning Algorithm Based on KNN

Of all the active learning research, the study on the active learning algorithm for SVM is much less. In this paper, based on K-Nearest Neighbors (KNN), we propose a new SVM active learning algorithm. The algorithm first collects the potential informative samples to form a potential informative sample set, and then in this sample set, based on KNN it evaluates the sparseness for each sample. The sample that locates at a sparser region is taken as an informative one, and is selected for training. Experimental results show that the proposed algorithm can greatly improve the classification performance, and can efficiently accelerate the convergence of the classifier.

An Algorithm of Turbulence Wavenumber Spectrum Matching Based on SVM

Considering the problem of invalid data caused mismatch of wavenumber spectrum which contained in turbulence observation data, an algorithm of turbulent wavenumber spectrum matching based on SVM is proposed. Category labels are obtained from pre-processed raw data by cross validation algorithm, and then the optimum parameters of the classifier are got through SVM learning algorithm. Sea trial data validation results indicate that the algorithm has high matching accuracy, and provides a new way to calculate the turbulence wavenumber spectrum matching.

Support Vector Machines and Kernel Functions for Text Processing

This work presents kernel functions that can be used in conjunction with the Support Vector Machine – SVM – learning algorithm to solve the automatic text classification task. Initially the Vector Space Model for text processing is presented. According to this model text is seen as a set of vectors in a high dimensional space; then extensions and alternative models are derived, and some preprocessing procedures are discussed. The SVM learning algorithm, largely employed for text classification, is outlined: its decision procedure is obtained as a solution of an optimization problem. The “kernel trick”, that allows the algorithm to be applied in non-linearly separable cases, is presented, as well as some kernel functions that are currently used in text applications. Finally some text classification experiments employing the SVM classifier are conducted, in order to illustrate some text preprocessing techniques and the presented kernel functions.

Progressive Incremental Learning Algorithm of the Support Vector Machine SVM based on Cross Validation

Progressive Incremental Learning Algorithm of the Support Vector Machine SVM based on Cross Validation

A Flexible Rule-Based Approach to Learn Medical English-Chinese OOV Term Translations from the Web

Out-of-vocabulary (OOV) terms, which do not exist in most dictionaries, usually cause failures in a cross language information retrieval (CLIR) system. Most existing approaches achieve a high performance when using web-mining to translate name entity type OOV terms. However, these methods gain a low performance when they are applied to medical OOV terms because they contain non-Chinese characters which are normally ignored by existing approaches, such as symbols, Roman alphabets and Arabic numbers. This paper presents a flexible rule-based approach towards the acquisition of medical OOV term translation. Our method uses a combination of a novel rule-based pattern extraction and brute force generation to identify the part of non-Chinese characters. To cope with the time-consuming task of ranking list and human extraction of OOV term translation, this paper presents a machine learning method to select correct translations automatically. In the method, twenty-one different features for each Chinese translation candidate are extracted, and the correct Chinese translations are selected by machine learning with our newly proposed statistics filter. By testing our method with 1,654 English ICD9 medical OOV terms, our proposed method (SF+F+W+B+P+S with the base machine learning algorithm SVM) outperforms the existing methods with a recall and precision value of 83.05% and 79.72%, respectively.

English

MicroRNAs (miRNA's) constitute a large family of non coding RNAs that function to regulate gene expression. Wet lab experiments usually used to classify the miRNA of plants and animals are highly expensive, labor intensive and time consuming. Thus there arises a need for computational approach for classification of plant and animal miRNA. These computational approaches are fast and economical as compared to wet lab techniques. Here a machine learning approach is used to classify miRNA of HIV, plants and animals. The new SVM learning algorithm called Weka LibSVM has been used for classification of plant and animal and HIVmiRNA. The model has been tested on available data and it gives results with 95% accuracy.

A New Learning Algorithm of SVM from Linear Separable Samples

Training a SVM corresponds to solving a linearly constrained quadratic problem (QP) in a number of variables equal to the number of data points, this optimization problem becoming challenging when the number of data points exceeds few thousands. Because the computational complexity of the existing algorithms is extremely large in case of few thousands support vectors and therefore the SVM QP-problem becomes intractable, several decomposition algorithms that do not make assumptions on the expected number of support vectors have been proposed instead. In this paper we propose a heuristic learning algorithm of gradient type for learning a SVM using linear separable data, and analyze its performance in terms of accuracy and efficiency. In order to evaluate the efficiency of our learning method, several tests were performed against the Platt’s SMO method, and the conclusions are formulated in the final section of the paper.

An Incremental Learning Algorithm for SVM based on Voting Principle

In this paper, we mainly adopt the voting combination method to implement the incremental learning for SVM. The incremental learning algorithm proposed by this paper has contained two parts in order to tackle different types of incremental learning cases, the first part is to deal with the on-line incremental learning and the second part is to deal with the batch incremental learning. In the final, we make the experiment to verify the validity and efficiency of such algorithm.

Hierarchical fusion of multi-spectral face images for improved recognition performance

This paper presents a two level hierarchical fusion of face images captured under visible and infrared light spectrum to improve the performance of face recognition. At image level fusion, two face images from different spectrums are fused using DWT based fusion algorithm. At feature level fusion, the amplitude and phase features are extracted from the fused image using 2D log polar Gabor wavelet. An adaptive SVM learning algorithm intelligently selects either the amplitude or phase features to generate a fused feature set for improved face recognition. The recognition performance is observed under the worst case scenario of using single training images. Experimental results on Equinox face database show that the combination of visible light and short-wave IR spectrum face images yielded the best recognition performance with an equal error rate of 2.86%. The proposed image-feature fusion algorithm also performed better than existing fusion algorithms.