SVM is a supervised machine learning algorithm that can be used for classification or regression. It works by finding the optimal hyperplane that separates classes by the largest margin. SVM identifies the hyperplane that results in the largest fractional distance between data points of separate classes. It can perform nonlinear classification using kernel tricks to transform data into higher dimensional space. SVM is effective for high dimensional data, uses a subset of training points, and works well when there is a clear margin of separation between classes, though it does not directly provide probability estimates. It has applications in text categorization, image classification, and other domains.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
This presentation is about Sentiment analysis Using Machine Learning which is a modern way to perform sentiment analysis operation. it has various techniques and algorithm described and compared for SA
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
This presentation is about Sentiment analysis Using Machine Learning which is a modern way to perform sentiment analysis operation. it has various techniques and algorithm described and compared for SA
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback
Sentiment analysis of Twitter data using pythonHetu Bhavsar
Twitter is a popular social networking website where users posts and interact with messages known as “tweets”. To automate the analysis of such data, the area of Sentiment Analysis has emerged. It aims at identifying opinionative data in the Web and classifying them according to their polarity, i.e., whether they carry a positive or negative connotation. We will attempt to conduct sentiment analysis on “tweets” using various different machine learning algorithms.
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Introduction to Statistical Machine Learningmahutte
This course provides a broad introduction to the methods and practice
of statistical machine learning, which is concerned with the development
of algorithms and techniques that learn from observed data by
constructing stochastic models that can be used for making predictions
and decisions. Topics covered include Bayesian inference and maximum
likelihood modeling; regression, classi¯cation, density estimation,
clustering, principal component analysis; parametric, semi-parametric,
and non-parametric models; basis functions, neural networks, kernel
methods, and graphical models; deterministic and stochastic
optimization; over¯tting, regularization, and validation.
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Sentiment analysis using naive bayes classifier Dev Sahu
This ppt contains a small description of naive bayes classifier algorithm. It is a machine learning approach for detection of sentiment and text classification.
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka tutorial will provide you with a detailed and comprehensive knowledge of the Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial:
1. What is Naive Bayes?
2. Bayes Theorem and its use
3. Mathematical Working of Naive Bayes
4. Step by step Programming in Naive Bayes
5. Prediction Using Naive Bayes
Check out our playlist for more videos: http://bit.ly/2taym8X
Sentiment analysis of Twitter data using pythonHetu Bhavsar
Twitter is a popular social networking website where users posts and interact with messages known as “tweets”. To automate the analysis of such data, the area of Sentiment Analysis has emerged. It aims at identifying opinionative data in the Web and classifying them according to their polarity, i.e., whether they carry a positive or negative connotation. We will attempt to conduct sentiment analysis on “tweets” using various different machine learning algorithms.
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Introduction to Statistical Machine Learningmahutte
This course provides a broad introduction to the methods and practice
of statistical machine learning, which is concerned with the development
of algorithms and techniques that learn from observed data by
constructing stochastic models that can be used for making predictions
and decisions. Topics covered include Bayesian inference and maximum
likelihood modeling; regression, classi¯cation, density estimation,
clustering, principal component analysis; parametric, semi-parametric,
and non-parametric models; basis functions, neural networks, kernel
methods, and graphical models; deterministic and stochastic
optimization; over¯tting, regularization, and validation.
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Sentiment analysis using naive bayes classifier Dev Sahu
This ppt contains a small description of naive bayes classifier algorithm. It is a machine learning approach for detection of sentiment and text classification.
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka tutorial will provide you with a detailed and comprehensive knowledge of the Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial:
1. What is Naive Bayes?
2. Bayes Theorem and its use
3. Mathematical Working of Naive Bayes
4. Step by step Programming in Naive Bayes
5. Prediction Using Naive Bayes
Check out our playlist for more videos: http://bit.ly/2taym8X
In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis
Machine Learning techniques used in Artificial Intelligence- Supervised, Unsupervised, Reinforcement Learning. It discusses about Linear Regression, Logistic Regression, SVM, Random forest, KNN, K-Means Clustering and Apriori Algorithm. It also Illustrates the applications of AI in various fields.
What is machine learning? Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Quality defects in TMT Bars, Possible causes and Potential Solutions.
sentiment analysis using support vector machine
1. Sentiment Analysis using support
vector machine
Guide : Prof. S.B.Patil
Presented by : Shital M. Andhale
T120398502
Information Technology Dept, VIIT pune
2. Contents
• What is sentiment analysis ?
• Sentiment Analysis in Twitter or any other Social Media.
• Sentiment Analysis Classification
• Sentiment Analysis using machine learning
• Types of Machine Learning
• Support vector Machine Algorithm
• How does it work ?
• Pros and cons of SVM
• Applications
• Conclusion
• references
3. What is Sentiment Analysis ?
• Sentiment Analysis is the process of finding the opinion
of user about some topic or the text in consideration.
• It is also known as opinion mining.
• In other words, it determines whether a piece of writing
is positive, negative or neutral.
4. Sentiment Analysis in Social media or Twitter
• Micro blogging websites are social media site (Twitter, Facebook) to which user
makes short and frequent posts.
• Twitter is one of the famous micro blogging services where user can read and post
messages which are 148 characters in length. Twitter messages are also called as
Tweets.
• we will use these tweets as raw data. We will use a techniques that automatically
extracts tweets into positive, negative or neutral sentiments. By using the sentiment
analysis the customer can know the feedback about the product before making a
purchase. Sentiment analysis is a type of natural language processing for tracking
the mood of the public about a particular product or topic.
5. Classification of Sentiment Analysis
Sentiment
analyis
Machine
learning
Approch
Superwised
learning
Linear
classifier
Support Vector
Machine
Neural
network
Decision tree
Rule based
classifires
Probablistic
classifiers
Unsuperwised
learning
6. SA using machine learning Approch
6
• Machine learning is a type of artificial intelligence (AI) that provides computers
with the ability to learn without being explicitly programmed.
• The Machine that Teaches Themselves.
7. Types of machine learning
• Supervised Learning
Inferring a function from labelled training data. A supervised learning
algorithm analyses the training data (a list of input and their correct output) and
produces an appropriate function, which can be used for mapping new examples.
• Unsupervised Learning
Inferring a function to describe hidden structure from unlabelled data. No labels
are given to the learning algorithm, leaving it on its own to find structure in its
input.
8. Support Vector Machine Algorithm
What is Support Vector Machine?
• SVM is a non-probabilistic binary linear classifier. It has the ability to linearly separate
the classes by a large margin. Add to it the Kernel, and SVM becomes one of the most
powerful classifier capable of handling infinite dimensional feature vectors.
• “Support Vector Machine” (SVM) is a supervised machine learning algorithm which can
be used for both classification or regression challenges. However, it is mostly
used in classification problems.
• In this algorithm, we plot each data item as a point in n-dimensional space (where n is
number of features you have) with the value of each feature being the value of a particular
coordinate. Then, we perform classification by finding the hyper-plane that differentiate
the two classes very well .
10. Identify the right hyper-plane
(Scenario-1):
• Here, we have three hyper-planes
(A, B and C). Now,identify the
right hyper-plane to classify star
and circle
• You need to remember a thumb
rule to identify the right hyper-
plane: “Select the hyper-plane
which segregates the two classes
better”. In this scenario, hyper-
plane “B” has excellently
performed this job.
11. Identify the right
hyper-plane
(Scenario-2):
Here, we have three hyper-planes (A, B and C)
and all are segregating the classes well. Now,
How can we identify the right hyper-plane?
Here, maximizing the distances between
nearest data point (either class) and hyper-
plane will help us to decide the right hyper-
plane. This distance is called as Margin
Above, you can see that the margin for hyper-
plane C is high as compared to both A and B.
Hence, we name the right hyper-plane as C.
Another lightning reason for selecting the hyper-
plane with higher margin is robustness. If we
select a hyper-plane having low margin then
there is high chance of miss-classification.
12. Identify the right hyper-plane
(Scenario-3)
• SVM selects the hyper-plane
which classifies the classes
accurately prior to maximizing
margin. Here, hyper-plane B has a
classification error and A has
classified all correctly.
• Therefore, the right hyper-plane
is A.
13. Can we classify two
classes (Scenario-4)?
Below, I am unable to segregate the
two classes using a straight line, as
one of star lies in the territory of
other(circle) class as an outlier
14. Can we classify two
classes (Scenario-4)
As I have already mentioned, one star
at other end is like an outlier for star
class. SVM has a feature to ignore
outliers and find the hyper-plane that
has maximum margin. Hence, we can
say, SVM is robust to outliers.
15. Find the hyper-plane to
segregate to classes (Scenario-
5):
• In the scenario below, we can’t have
linear hyper-plane between the two
classes, so how does SVM classify
these two classes? Till now, we have
only looked at the linear hyper-plane.
• SVM can solve this problem. Easily!
It solves this problem by
introducing additional feature. Here,
we will add a new feature
z=x^2+y^2. Now, let’s plot the data
points on axis x and z:
16. Find the hyper-plane to
segregate to classes (Scenario-
5):
In above plot, points to consider are:
• All values for z would be positive
always because z is the squared
sum of both x and y
• In the original plot, red circles
appear close to the origin of x and
y axes, leading to lower value of z
and star relatively away from the
origin result to higher value of z.
17. Find the hyper-plane to segregate
to classes (Scenario-5):
When we look at the hyper-plane in
original input space it looks like a
circle:
18. Pros and cons of SVM
• Pros:
• It works really well with clear margin of separation
• It is effective in high dimensional spaces.
• It is effective in cases where number of dimensions is greater than the number of samples.
• It uses a subset of training points in the decision function (called support vectors), so it is
also memory efficient.
• Cons:
• It doesn’t perform well, when we have large data set because the required training time is
higher
• It also doesn’t perform very well, when the data set has more noise i.e. target classes are
overlapping
• SVM doesn’t directly provide probability estimates, these are calculated using an
expensive five-fold cross-validation.
19. Applications of SVM
• SVMs can be used to solve various real world problems:
• SVMs are helpful in text and hypertext categorization as their application can
significantly reduce the need for labeled training instances in both the standard
inductive and transductive settings
• Classification of images can also be performed using SVMs.
• Experimental results show that SVMs achieve significantly higher search
accuracy than traditional query refinement schemes after just three to four
rounds of relevance feedback.
• This is also true of image segmentation systems, including those using a
modified version SVM that uses the privileged approach as suggested by
Vapnik. Hand-written characters can be recognized using SVM.
20. Naïve Bays SVM Maximum
Entropy
Easy to Implement Harder to Implement Harder to Implement
Less Efficient,
Efficient due to
working with large sets
of Words
Efficiency is maximum Efficiency is moderate
Limited Use Versatile
Used in Comp Vision,
Text Cat, IP
Hardly used
Comparison of ML algorithms
21. Conclusion
The machine learning can prove efficient over traditional techniques for SA
The Support Vector Machine algorithm can be useful in sentiment analysis of text
categorization.
22. References
• Mining Social Media Data for Understanding Students’ Learning Experiences
,Xin Chen, Student Member, IEEE, Mihaela Vorvoreanu, and Krishna Madhavan
• Machine Learning Algorithms for Opinion Mining and Sentiment Classification
Jayashri Khairnar,Mayura Kinikar[IJSRP].
• Managing Data in SVM Supervised Algorithm for Data Mining Technology
Sachin Bhaskart, Vijay Bahadur Singh2, A. K. Nayak.
• Wekipedia and Internet