Supervised learning

An Enhanced Approach for Web Services Clustering using Supervised Machine Learning Techniques ABSTRACT Automatic document classification provides techniques that may improve and support web service clustering. As the number of services increases, the cost of classifying services through manual work increases. In this research, we presented an enhanced approach for service clustering that combines text mining and machine learning technology. The method only uses text description of each service

COMPARISON OF CLASSIFIERS CLASSIFIER CATEGORY DESCRIPTION REFERENCE Naive Bayes Probability based classifier This classifier is derived from Naïve Bayes conditional probability. This is suitable for datasets having less number of attributes. [5] Bayesian Net Probability based classifier Network of nodes based on Naïve Bayes classifier is termed as Bayesian Net. This can be applied to larger datasets as compared to Naïve Bayes. [9] Decision Tree (J48) Tree based approach It is enhanced version of

proposed the image needs to be classified which is done using the image classification technique of image mining. Classification can be carried out by applying the method of supervised classification and unsupervised classification but here we will mainly focus upon supervised classification. Supervised Classification Supervised classification is

Data mining is the process of discovering patterns, trends, correlations from large amounts of data stored electronically in repositories, using statistical methods, mathematical formulas, and pattern recognition technologies (Sharma n.d.). The main idea is to analyze data from different perspectives and discover useful trends, patterns and associations. As discussed in the previous chapter, the healthcare organizations are producing massive amounts of electronic medical records, which are impossible

Approach for Document Classification Using Semi-supervised Learning Technique Ms. Sayali A. Dolas Department of Computer Engineering MIT Academy of Engineering, Alandi Savitribai Phule Pune University sayalidolas5193@gmail.com Dr. Shitalkumar A. Jain Department of Computer Engineering MIT Academy of Engineering, Alandi Savitribai Phule Pune University sajain@comp.maepune.ac.in Abstract— Multi-label classification is a significant machine learning job where one allots a subset of candidate tags

• Classification: It is the association of information in given classes. Also called supervised classification, the cataloguing uses given class tags to edict the items in the information collection. Classification line of attack normally use a training set where all items are already linked with known class tags. The classification algorithm

Parallel Support Vector Machine Junfeng Wu Junming Chen May 6, 2016 1 INTRODUCTION Support vector machines is a supervised machine learning alogrithom used for classification. The problem could be written : minimize 1 |w |2 2 yi((w,xi)+b)−1≥0 where w is a linear combination of the training data: n w = αi k(xi ) i=1 this could be further written in a dual form[5]: min 1αTQα−eTα α2 0≤αi ≤C, yTα=0, ∀i ≤n where Q is the kernel matrix. This dual form is a quadratic programming problem with linear

Dynamic News Classification using Machine Learning Introduction Why this classification is needed ? (Ashutosh) The exponential growth of the data may lead us to a time in future where huge amount of data would not be able to be managed easily. Text Classification is done through Text Mining study which would help sorting the important texts from the content or a document to manage the data or information easily. //Give a scenario, where classification would be mandatory. Advantages of classification

proposed in which the overall picture of classification of twitter data has been proposed and accuracy of the evaluation strategies by various supervised learning algorithms has been evaluated. Review data is collected for various product domains from micro blogging sites like twitter, face book. Keywords Opinions, Web mining, Sentiment Analysis, Supervised Learning, Mining. 1. INTRODUCTION In recent years, we have witnessed that a huge amount of opinionated text is available which have greatly influenced

Index Terms—Decision Tree Classifier; Disease Detec- tion;Machine Learning ; Smart Farming; I. INTRODUCTION Smart Farming provides technological solutions to the agri- cultural businesses as well as to the farmers. It has many applications in precision farming, weather forecasting, quality control, data analyses