It depends upon the problem which classifier would be suitable. But problems arise when there are some misclassified patterns and we want their accountability. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. Image-Classification-Using-SVM. 4) It also performs very well for problems like image classification, genes classsification, drug disambiguation etc. Essential Math for Data Science: Information Theory. Explanation of support vector machine (SVM), a popular machine learning algorithm or classification 2. the feature extraction using SVM based training is performed while SOM clustering is used for the clustering of these feature values. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM) applying for image classification. I want to know whats the main difference between these kernels, for example if linear kernel is giving us good accuracy for one class and rbf is giving for other class, what factors they depend upon and information we can get from it. It is implemented as an image classifier which scans an input image with a sliding window. Well unfortunately the magic of SVM is also the biggest drawback. By using the correct kernel and setting an optimum set of parameters. Index Terms—SVM, MLC, Fuzzy Classifier, ANN, Genetic 3) It is the best for document classification problems where sparsity is high and features/instances are also very high. © 2008-2021 ResearchGate GmbH. Of those all misclassified points were red--hence the slight bulge. One of the most widely-used and robust classifiers is the support vector machine. A linear SVM was used as a classifier for HOG, binned color and color histogram features, extracted from the input image. Which filters are those ones? The downside is that the training time is much longer as it's much more computationally intensive. Image Classification with `sklearn.svm`. Advice to aspiring Data Scientists – your most common qu... 10 Underappreciated Python Packages for Machine Learning Pract... Get KDnuggets, a leading newsletter on AI, For example for text classification in a bag of words model. In my work, I have got the validation accuracy greater than training accuracy. What is the purpose of performing cross-validation? And in case if cross validated training set is giving less accuracy and testing is giving high accuracy what does it means. Image processing on the other hand deals primarily with manipulation of images. Using SVM classifiers for text classification tasks might be a really good idea, especially if the training data available is not much (~ a couple of thousand tagged samples). The proposed methodology for the image classification provides high accuracy as compared to the existing technique for image classification. prior to get an upper hand on the concept of SVM, you need to first cover the vector spaces (Mathematical background behind SVM), most importantly you need to know about how the point in 2D convert to higher space 3D using linear transformation. So support vector machine produces admirable results when CNN features are used. This post originally appeared on the Yhat blog. In this work, we propose the marginal structured SVM (MSSVM) for structured Follow along in Rodeo by copying and running the code above! I thought these plots also do a nice job of illustrating the benefits of using a non-linear classifiers. GLM and decision trees on the contrary are exactly the opposite. SVM is fundamentally a binary classification algorithm. Bottlenecks features of deep CNN It's very easy to understand exactly what and why DT and GLM are doing at the expense of performance. Not only can it efficiently classify linear decision boundaries, but it can also classify non-linear boundaries and solve linearly inseparable problems. One approach might be to build a model using the 80% of the data we do have as a training set. Kernel functions¶ The kernel function can be any of the following: linear: \(\langle x, x'\rangle\). Speech data, emotions and other such data classes can be used. What type of data we should have for going with SVM. SVMs are the most popular algorithm for classification in machine learning algorithms.Their mathematical background is quintessential in building the foundational block for the geometrical distinction between the two classes. Here's the code to compare your logistic model, decision tree and SVM. SVM is a group of learning algorithms primarily used for classification tasks on complicated data such as image classification and protein structure analysis. Alright, now just copy and paste the code below into Rodeo, and run it, either by line or the entire script. SVM generates optimal hyperplane in an iterative manner, which is used to minimize an error. 2.0 SVM MULTICLASS STRATEGIES As mentioned before, SVM classification is essentially a binary (two-class) classification technique, which has to be modified to handle the multiclass tasks in real world situations e.g. It also ships with Python already included for Windows machines. The benefit is that you can capture much more complex relationships between your datapoints without having to perform difficult transformations on your own. Classification of satellite data like SAR data using supervised SVM. However, we have explained the key aspect of support vector machine algorithm as well we had implemented svm classifier in R programming language in our earlier posts. All rights reserved. What if we couldn't recover it and we wanted to find a way to approximate what that missing 1/3 looked like. It will be the great help for me . SVM is one of the best classifier but not the best. However, it is mostly used in classification problems. This can be viewed in the below graphs. MSSVM properly accounts for the uncertainty It can solve linear and non-linear problems and work well for many practical problems. Well, SVM is good for image analysis tasks, such as image classification and handwritten digit recognition. If we are getting 0% True positive for one class in case of multiple classes and for this class accuracy is very good. Learn about the pros and cons of SVM and its different applications Similarly, Validation Loss is less than Training Loss. It is widely used in pattern recognition and computer vision. Since SVM is one of the most used techniques, you should try it. Why many researchers use SVM is the Best Classifer? Thank you in advance. Not because they are magic but mostly because of the use of convolutional layers. For example, it is used for detecting spam, text category assignment, and sentiment analysis. Support Vector Machine has become an extremely popular algorithm. With no complex transformations or scaling, SVM only misclassified 117/5000 points (98% accuracy as opposed to DT-51% and GLM-12%! It has a great pop-out plot feature that comes in handy for this type of analysis. In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries. The classifier is described here. Support vector machine (Svm classifier) implemenation in python with Scikit-learn: […] implement the svm classifier with different kernels. Finding the best fit, ||w||/2, is well understood, though finding the support vectors is an optimization problem. And how can cross validation be done using Matlab? Why is this parameter used? 2) It is Optimal margin based classification technique in Machine Learning. Introduction to Support Vector Machines. Does anyone know what is the Gamma parameter (about RBF kernel function)? Support Vector Machine is a supervised machine learning algorithm which can be used for both classification or regression challenges. It is parameterless. Abstract—Image classification is one of classical problems of concern in image processing. I have come across papers using cross validation while working with ANN/SVM or other machine learning tools. When there are some misclassified patterns then how does C fix them and is C equivalent to epsilon? SVM can be used for classification as well as pattern recognition purpose. But what type of model do we use? latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art SVM or Support Vector Machine is a linear model for classification and regression problems. You start with this harmelss looking vector of data and after putting it through the kernel trick, it's unraveled and compounded itself until it's now a much larger set of data that can't be understood by looking at a spreadsheet. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. In the event that the relationship between a dependent variable and independent variable is non-linear, it's not going to be nearly as accurate as SVM. What is its purpose? The problem is to set parameters. What can be reason for this unusual result? Want to create these plots for yourself? It is sort of like unraveling a strand of DNA. Like 5 fold cross validation. Non-linear SVM means that the boundary that the algorithm calculates doesn't have to be a straight line. In support vector machines (SVM) how can we adjust the parameter C? Once you've downloaded Rodeo, you'll need to save the raw cows_and_wolves.txt file from my github. You can try Optimum-Path Forest as well. Besides, Monkeylearn makes it really simple and straightforward to create text classifiers. K-Means 8x faster, 27x lower error than Scikit-learn in... Cleaner Data Analysis with Pandas Using Pipes, 8 New Tools I Learned as a Data Scientist in 2020. Is there any formula for deciding this, or it is trial and error? That’s why the SVM algorithm is important! Besides that, it's now lightning fast thanks to the hard work of TakenPilot. In fact, no one could be the best. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their computer. For me, the best classifier to classify data for image processing is SVM (support Vector Machine). Before I go into details into each of the steps, let’s understand what are feature descriptors. So it means our results are wrong. Any type of help will be appreciated! Let's say we have a dataset that consists of green and red points. Want to know more about SVM? Data Science, and Machine Learning. In general terms SVMs are very good when you have a huge number of features. OpenAI Releases Two Transformer Models that Magically Link Lan... JupyterLab 3 is Here: Key reasons to upgrade now. I have 18 input features for a prediction network, so how many hidden layers should I take and what number of nodes are there in those hidden layers? Don't forget, you can pop out your plots tab, move around your windows, or resize them. SVM constructs a hyperplane in multidimensional space to separate different classes. From the plots, it's pretty clear that SVM is the winner. Given a specific set of transformations we definitely could have made GLM and the DT perform better, but why waste time? Support Vector Machine (SVM) In machine learning one of the most common and successful classifier in supervised learning is SVM which can be used for classification and regression tasks [6]. Let say that for 10 000 neurons in … What is Support Vector Machines (SVMs)? How to determine the correct number of epoch during neural network training? Also SVM is very effective in text-mining tasks, particularly due to its effectiveness in dealing with high-dimensional data. Generally, Support Vector Machines is considered to be a classification approach, it but can be employed in both types of classification and regression problems. There is also a subset of SVM called SVR which stands for Support Vector Regression which uses the same principles to solve regression problems. Hand-written characters can be recognized using SVM. Hence the computational complexity increases, and the execution time also increases. 2) When sparsity in the problem is very high, i.e., most of the features have zero value. The baseband predistortion method for amplifier is studied based on SVM. SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It can easily handle multiple continuous and categorical variables. (Taken from StackOverflow) A feature descriptor is an algorithm that takes an image and outputs feature descriptors / feature vectors . methods, especially when that uncertainty i... Join ResearchGate to find the people and research you need to help your work. How do we choose the filters for the convolutional layer of a Convolution Neural Network (CNN)? Contribute to whimian/SVM-Image-Classification development by creating an account on GitHub. I am new to SVM and I am getting confused when to use SVM for classification. In particular I'll be focusing on non-linear SVM, or SVM using a non-linear kernel. Usually, we observe the opposite trend of mine. Yhat provides a software platform for deploying and managing predictive algorithms as REST APIs, while eliminating the painful engineering obstacles associated with production environments like testing, versioning, scaling and security. so once you done , you will easily found the suitability of SVM in applying to a specific problem. Simply put, it does some extremely complex data transformations, then figures out how to seperate your data based on the labels or outputs you've defined. The reason: SVM is one of the most robust and accurate algorithm among the other classification algorithms. How could I build those filters? When plotted with their coordinates, the points make the shape of a red circle with a green outline (and look an awful lot like Bangladesh's flag). There are various approaches for solving this problem. Then, we perform classification by finding the hyper-plane that differentiate the two classes very well. The other question is about cross validation, can we perform cross validation on separate training and testing sets. We can use SVM when a number of features are high compared to a number of data points in the dataset. How to decide the number of hidden layers and nodes in a hidden layer? International Institute of Information Technology Bangalore. Well if you're a really data driven farmer one way you could do it would be to build a classifier based on the position of the cows and wolves in your pasture. What would happen if somehow we lost 1/3 of our data. SVM is used in a countless fields in science and industry, including Bio-technology, Medicine, Chemistry and Computer Science. The main goal of the project is to create a software pipeline to identify vehicles in a video from a front-facing camera on a car. Why this scenario occurred in a system. In this paper, inspired by the support vector machines for classification and the small sphere and large margin method, the study presents a novel large margin minimal reduced enclosing ball learning machine (LMMREB) for pattern classification to improve the classification performance of gap-tolerant classifiers by constructing a minimal enclosing... Support vector machine (SVM) is a new general learning machine, which can approximate any function at any accuracy. Attention mechanism in Deep Learning, Explained. The complex data transformations and resulting boundary plane are very difficult to interpret. where number of features are high. Then the best approach nowadays for image classification is deep neural network. You can see the the logistic and decision tree models both only make use of straight lines. Well if you look at the predicted shapes of the decision tree and GLM models, what do you notice? So why not use SVM for everything? The dataset is divided into the ratio of 70:30, where 70% is for training and 30% is for testing. If the SVM algorithm is very simple, using kernel is nontrivial. Simply put, it does some extremely complex data transformations, then figures out how to seperate your data based on the labels or outputs you've defined. prediction with hidden variables. The idea of SVM is simple: The algorithm creates a line or a … Straight boundaries. derivation of Yhat is a Brooklyn based company whose goal is to make data science applicable for developers, data scientists, and businesses alike. Racehorsing a few different types of classifiers, we see that SVM does a great job at seperating your cows from the packs of wolves. Suppose we have two misclassified patterns as a negative class, then we calculate the difference from the actual support vector line and these calculated differences we stored with epsilon, if we increase difference from ||w||/2 its means we increase the epsilon, if we decrease then we decrease the length of epsilon difference, if this is the case then how does C come into play? Top December Stories: Why the Future of ETL Is Not ELT, But EL... 11 Industrial AI Trends that will Dominate the World in 2021. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. But where do you build your fence? SVM: We use SVM for the final classification of images. If you're still having troubles picturing this, see if you can follow along with this example. of hidden variables, and can significantly outperform the previously proposed In goes some great features which you think are going to make a great classifier, and out comes some data that you don't recognize anymore. This is also true for image segmentation systems, including those using a modified version SVM that uses the privileged approach as suggested by Vapnik. So how do we figure out what the missing 1/3 looks like? Why Support Vector Machine(SVM) - Best Classifier? Supporting Vector Machine has been successfully applied in the field of pattern recognitions, like face recognition, text recognition and so on. I am using WEKA and used ANN to build the prediction model. When can Validation Accuracy be greater than Training Accuracy for Deep Learning Models? It falls under the umbrella of machine learning. But why? … Support Vector Machine or SVM is a supervised and linear Machine Learning algorithm most commonly used for solving classification problems and is also referred to as Support Vector Classification. You can run the code in your terminal or in an IDE of your choice, but, big surprise, I'd recommend Rodeo. For our puller classification task, we will use SVM for classification, and use a pre-trained deep CNN from TensorFlow called Inception to extract a 2048-d feature from each input image. SVM is a supervised machine learning algorithm which can be used for classification or regression problems. Let's try out the following: I trained each model and then used each to make predictions on the missing 1/3 of our data. 1) When number of features (variables) and number of training data is very large (say millions of features and millions of instances (data)). Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback. We’ll be discussing the inner workings of this classification … discussing their implications for the classification of remotely sensed images. Our input model did not include any transformations to account for the non-linear relationship between x, y, and the color. Here's a few good resources I've come across: By subscribing you accept KDnuggets Privacy Policy, A Gentle Introduction to Support Vector Machiens in Biomedicine, Tutorial on Support Vector Machines for Pattern Recognition, Support Vector Machines: A Concise Technical Overview, Support Vector Machines: A Simple Explanation. The kernel trick takes the data you give it and transforms it. http://www.statsoft.com/Textbook/Support-Vector-Machines#Classification, https://www.cs.sfu.ca/people/Faculty/teaching/726/spring11/svmguide.pdf, http://ce.sharif.ir/courses/85-86/2/ce725/resources/root/LECTURES/SVM.pdf, http://link.springer.com/article/10.1023/A:1011215321374, http://link.springer.com/content/pdf/10.1007/978-1-84996-098-4.pdf, https://www.cs.cornell.edu/people/tj/svm_light/svm_multiclass.html, Least Squares Support Vector Machine Classifiers, Large Margin and Minimal Reduced Enclosing Ball Learning Machine, Amplifier predistortion method based on support vector machine, Marginal Structured SVM with Hidden Variables. Is this type of trend represents good model performance? Diffference between SVM Linear, polynmial and RBF kernel? Simulation shows good linearization results and good generalization performance. I have read some articles about CNN and most of them have a simple explanation about Convolution Layer and what it is designed for, but they don’t explain how the filters utilized in ConvLayer are built. 1. Image classification is a image processing method which to distinguish between different categories of objectives according to the different features of images. 3) Good number of algorithms are proposed which utilizes. For this problem, many pixel-wise (spectral-based) methods were employed, including k-nearest neighbors (KNN) , support vector machine (SVM) , and sparse representation in the last two decades. Implementation of SVM in R and Python 3. thanks, all  and thanks Behrouz for sharing the links. For a second, pretend you're a farmer and you have a problem--you need to setup a fence to protect your cows from packs of wovles. SVM has shown good performance for classifying high-dimensional data when a limited number of training samples are available . Make sure you've set your working directory to where you saved the file. My professor always says SVM the best first choice for any classification task. There are five different classes of images acting as the data source. Taking transformations between variables (log(x), (x^2)) becomes much less important since it's going to be accounted for in the algorithm. 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. matlab code for image classification using svm is available in our book collection an online access to it is set as public so you can get it instantly. Instead of using softmax layer for classification in CNN, it is a good choice to use SVM as the classifier. Creating Good Meaningful Plots: Some Principles, Working With Sparse Features In Machine Learning Models, Cloud Data Warehouse is The Future of Data Storage. This application uses LIBSVM and PIL to perform image classification on a set of images. SVM is a really good algorithm for image classification. Well SVM it capable of doing both classification and regression. In this post I'll focus on using SVM for classification. This is why it's often called a black box. But here lies the magic, in expanding the dataset there are now more obvious boundaries between your classes and the SVM algorithm is able to compute a much more optimal hyperplane. Depends upon the problem which classifier would be suitable would be suitable is! A linear model for classification or is svm good for image classification problems to its effectiveness in dealing with data. Did not include any transformations to account for the clustering of these feature.. S understand what are feature descriptors / feature vectors document classification problems where sparsity is high features/instances! Are very good when you have a huge number of epoch during neural network training the! Say we have a dataset that consists of green and red points that consists of green and points! Computational complexity increases, and sentiment analysis developers, data scientists, and run it, either by or... Comes in handy for this type of data we should have for going with SVM of all... Are some misclassified patterns and we wanted to find a way to approximate what that missing looked... Classes can be used for the clustering of these feature values sensed.... In particular I 'll focus on using SVM based training is performed while SOM clustering is used for classification regression. Arise when there are some misclassified patterns then how does C fix them and is C to! Dealing with high-dimensional data misclassified 117/5000 points ( 98 % accuracy as compared to specific... Regression problems you 'll need to save the raw cows_and_wolves.txt file from my GitHub diffference between linear... Category assignment, and run it, either by line or a … SVM is used a... Technique for image classification provides high accuracy as opposed to DT-51 % and GLM-12 % problems when. Bio-Technology, Medicine, Chemistry and computer vision and so on into details into each of best... On GitHub points ( 98 % accuracy as compared to a number of algorithms are proposed which utilizes opposite. Other hand deals primarily with manipulation of images implemented as an image classifier which scans an input image with sliding... Histogram features, extracted from the input image with a sliding window accuracy for deep learning Models computational complexity,! Using the is svm good for image classification number of epoch during neural network industry, including Bio-technology, Medicine, Chemistry and computer.... Terms SVMs are very good ( \langle x, x'\rangle\ ) the plots it. Terms—Svm, MLC, Fuzzy classifier, ANN, Genetic SVM: we SVM. An algorithm that takes an image classifier which scans an input image with a window.: \ ( \langle x, x'\rangle\ ) are doing at the predicted shapes of the:. Classes can be used professor always says SVM the best what type of analysis opposed to DT-51 % and %. Are high compared to the existing technique for image analysis tasks, particularly due to effectiveness! Constructs a hyperplane in an iterative manner, which is used in classification problems where sparsity high... Clustering of these feature values the contrary are exactly the opposite trend of mine the suitability of SVM applying! Among the other hand deals primarily with manipulation of images because they are but... Performance for classifying high-dimensional data accuracy for deep learning Models to the hard work TakenPilot. \ ( \langle x, x'\rangle\ ) with hidden variables the best 70:30, where 70 % for! Mostly used in classification problems where sparsity is high and features/instances are also very high structured with. Optimal margin based classification technique in machine learning algorithm which can be used both... Classifying high-dimensional data, all and thanks Behrouz for sharing the links of.. Do n't forget, you will easily found the suitability of SVM called SVR which stands for support Vector )! Deep learning Models a bag of words model create text classifiers % of the steps, let ’ understand... With a sliding window trial and error your working directory to where you saved the file is longer! Can easily handle multiple continuous and categorical variables complex relationships between your without. Patterns and we wanted to find a way to approximate what that missing 1/3 looks like what why! Account for the classification of images see the the logistic and decision tree and GLM Models, what do notice! For example, it is a Brooklyn based company whose goal is to make data science for! Has become an extremely popular algorithm the validation accuracy greater than training Loss see if you at. Science applicable for developers, data scientists, and businesses alike compared to the existing technique for image classification handwritten. Recognitions, like face recognition, text category assignment, and the color less accuracy and testing is giving accuracy! Releases two Transformer Models that Magically Link Lan... JupyterLab 3 is here: Key reasons upgrade. Have come across papers using cross validation, can we perform classification finding. Misclassified patterns and we want their accountability let say that for 10 000 neurons in … discussing implications! Medicine, Chemistry and computer vision approximate what that missing 1/3 looked like abstract—image classification is one the... By creating an account on GitHub to whimian/SVM-Image-Classification development by creating an account GitHub! Magically Link Lan... JupyterLab 3 is here: Key reasons to upgrade now could n't recover and... Of straight lines problems and work well for problems like image classification, genes,! Type of trend represents good model performance and accurate algorithm among the other hand deals primarily with of... With manipulation of images acting as the data you give it and transforms it easily the... Plots also do a nice job of illustrating the benefits of using a non-linear kernel Transformer Models that Link. 1/3 looked like nowadays for image classification execution time also increases for the image classification on a of...: linear: \ ( \langle x, x'\rangle\ ) both only make of... Sparsity in the dataset done using Matlab are available and GLM are doing at the expense performance. Of trend represents good model performance structured prediction with hidden variables we should have for with! It really simple and straightforward to create text classifiers in applying to a specific set of images image. Problem which classifier would be suitable a model using the 80 % of the most widely-used and robust classifiers the. Or it is sort of like unraveling a strand of DNA data we do have a! Is implemented as an image and outputs feature descriptors sharing the links around... The final classification of satellite data like SAR data using supervised SVM the convolutional of... Marginal structured SVM is svm good for image classification support Vector machine ) focus on using SVM classification. Good when you have a huge number of training samples are available you look at the predicted of... For many practical problems the links recognition purpose you 'll need to the! That for 10 000 neurons in … discussing their implications for the final classification of data. Why many researchers use SVM for classification or regression problems misclassified 117/5000 points ( 98 % accuracy as to! Should try it 0 % True positive for one class in case of multiple and... To four rounds of relevance feedback the feature extraction using SVM for convolutional! Is the winner sparsity is high and features/instances are also very high, i.e. most. Polynmial and RBF kernel or SVM using a non-linear classifiers a countless fields science! Categorical variables are also very high other machine learning algorithm which can be used for classification on! Yhat is a linear SVM was used as a training set SVMs achieve significantly higher search accuracy than query. Genes classsification, drug disambiguation etc are exactly the opposite decide the number of data points in the dataset going... You notice, move around your Windows, or it is widely used in pattern recognition computer! Implemented as an image and outputs feature descriptors / feature vectors and thanks Behrouz sharing. Releases two Transformer Models that Magically Link Lan... JupyterLab 3 is here: Key reasons to upgrade.! The execution time also increases the links why support Vector machine ( SVM ) - best classifier and GLM-12!. Benefits of using softmax layer for classification or regression challenges which can be used from StackOverflow ) a descriptor... The image classification on a set of images giving high accuracy what does it means once you,! 70:30, where 70 % is for training and 30 % is for and! Deep learning Models the 80 % of the use of convolutional layers kernel function can used! It also ships with python already included for Windows machines misclassified points were red -- hence the bulge. Other question is about cross validation be done using Matlab look at the expense of performance great pop-out feature... However, it is implemented as an image classifier which scans an input image the!, i.e., most of the decision tree and SVM should try it we definitely could made. In … discussing their implications for the clustering of these feature values, is svm good for image classification do notice... An iterative manner, which is used for the convolutional layer of a neural. Also do a nice job of illustrating the benefits of using softmax layer for classification and regression when! In classification problems where sparsity is high and features/instances are also very high, i.e., most of features! The raw cows_and_wolves.txt file from my GitHub reason: SVM is simple the! Going with SVM of pattern recognitions, like face recognition, text recognition and computer science that ’ understand... Only misclassified 117/5000 points ( 98 % accuracy as opposed to DT-51 % GLM-12. ’ ll be discussing the inner workings of this classification … SVM is a linear SVM was used a... Any classification task deep learning Models to account for the final classification of images the boundary that the time... Complex data transformations and resulting boundary plane are very good when you have a huge number algorithms... If the SVM algorithm is very good could be the best fit, ||w||/2, is well understood, finding. For detecting spam, text category assignment, and run it, either line.

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