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What distinguishes support vector machine and neural network classification in ML statistical modeling?

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Machine learning (ML) is a branch of artificial intelligence that uses statistical models and algorithms to learn from data and make predictions or decisions. Classification is one of the main tasks of ML, where the goal is to assign a label or category to an input based on some features or attributes. There are many methods and techniques for classification, but two of the most popular and powerful ones are support vector machine (SVM) and neural network (NN). In this article, you will learn what distinguishes these two approaches and how they work in ML statistical modeling.

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Ajay Parashar

LinkedIn Top Voice ?? in ML | PGP-AIML | Data Science | Machine Learning | Deep Learning | CV | NLP | SQL | Power BI |…

1 SVM Basics

SVM is a linear classifier that tries to find the best hyperplane that separates the data points of different classes with the maximum margin. The margin is the distance between the hyperplane and the closest data points, called support vectors. SVM can also handle nonlinear classification by using kernel functions that transform the data into a higher-dimensional space where a linear separation is possible. SVM is a supervised learning method that requires labeled data and a predefined number of classes.

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Ajay Parashar

LinkedIn Top Voice ?? in ML | PGP-AIML | Data Science | Machine Learning | Deep Learning | CV | NLP | SQL | Power BI | Tableau
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SVMs are a type of supervised ml algorithm that can be used for both classification and regression tasks. SVMs work by finding a hyperplane in the data that separates the different classes with the largest possible margin. Example: Imagine a dataset that contains two classes of data points: red and blue. We want to train an SVM to classify new data points into either the red or blue class. The SVM will first find a hyperplane that separates the red and blue data points with the largest possible margin. The margin is the distance between the hyperplane and the closest data points from each class. Once the hyperplane is found, the SVM can be used to classify new data points by predicting which side of the hyperplane they fall on.

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2 NN Basics

NN is a nonlinear classifier that mimics the structure and function of biological neural networks. It consists of layers of interconnected nodes or neurons that process and transmit information through activation functions and weights. The input layer receives the data features, the output layer produces the class labels, and the hidden layers perform the computations and transformations. NN is also a supervised learning method that requires labeled data, but it can learn the number of classes from the data.

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Ajay Parashar

LinkedIn Top Voice ?? in ML | PGP-AIML | Data Science | Machine Learning | Deep Learning | CV | NLP | SQL | Power BI | Tableau
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A NN is a type of AI that is inspired by the structure and function of the human brain. NNs are made up of interconnected nodes, called neurons, which process information and pass it on to other neurons. To train an NN, we need to provide it with a large amount of data and tell it the desired output for each input. Example: Imagine we want to train a NN to recognize handwritten digits. We first need to collect a large dataset of the same, with each digit labeled with its correct answer, then feed this dataset to the NN and tell it to predict the correct answer for each digit. The NN would start by making random predictions initially and after training, it will be able to accurately predict the correct answer for most handwritten digits.

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3 SVM Advantages

SVM has some advantages over NN in ML statistical modeling. First, SVM is more robust to overfitting, which means that it can generalize better to unseen data and avoid learning noise or irrelevant patterns. This is because SVM tries to maximize the margin and use only the support vectors for classification, while NN uses all the data points and can have many parameters that need to be tuned. Second, SVM is more efficient and faster to train, especially for small or medium-sized data sets. This is because SVM only needs to solve a convex optimization problem, while NN needs to perform gradient descent or backpropagation, which can be computationally intensive and prone to local minima.

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4 NN Advantages

NN also has some advantages over SVM in ML statistical modeling. First, NN is more flexible and adaptable to complex and nonlinear problems, such as image recognition, natural language processing, or speech synthesis. This is because NN can learn multiple layers of features and representations from the data, while SVM relies on kernel functions that may not capture all the nuances and variations. Second, NN is more scalable and suitable for large and high-dimensional data sets. This is because NN can take advantage of parallel computing and distributed systems, while SVM can suffer from the curse of dimensionality and memory limitations.

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5 SVM vs NN Comparison

To compare SVM and NN in ML statistical modeling, you need to consider several factors, such as the type and size of the data, the complexity and linearity of the problem, the accuracy and generalization performance, and the computational resources and time available. There is no definitive answer to which method is better, as they have different strengths and weaknesses and may perform differently depending on the context and the goal. Therefore, you need to experiment and evaluate both methods and choose the one that best suits your needs and preferences.

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6 SVM and NN Examples

To illustrate how SVM and NN work in ML statistical modeling, let's look at some examples of classification problems and how they can be solved with these methods. For example, if you want to classify handwritten digits from 0 to 9 based on pixel values, you can use either SVM or NN, but NN may have an edge over SVM because it can learn more complex and nonlinear features from the images. On the other hand, if you want to classify spam emails based on word frequencies, you can use either NN or SVM, but SVM may have an advantage over NN because it can avoid overfitting and handle sparse and high-dimensional data better. Of course, these are just general guidelines and you should always test and compare both methods on your own data and problem.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What distinguishes support vector machine and neural network classification in ML statistical modeling?

1

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3

4

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1 SVM Basics

2 NN Basics

3 SVM Advantages

4 NN Advantages

5 SVM vs NN Comparison

6 SVM and NN Examples

7 Here’s what else to consider

Machine Learning

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What distinguishes support vector machine and neural network classification in ML statistical modeling?

1

2

3

4

5

6

7

1 SVM Basics

2 NN Basics

3 SVM Advantages

4 NN Advantages

5 SVM vs NN Comparison

6 SVM and NN Examples

7 Here’s what else to consider

Machine Learning

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