Support Vector Machines vs Neural Networks

Support Vector Machine (SVM) is a powerful machine learning algorithm adopted for linear or nonlinear classification, regression, and even outlier detection tasks and Neural networks, A machine learning (ML) model is made to simulate the structure and operations of the human brain. With a linear rise in the input size, an SVM’s number of parameters also increases linearly. Nevertheless, a NN does not. A neural network can have as many layers as desired, even though we only concentrated on single-layer networks here.

Support Vector Machine

Support Vector Machine (SVM) is a powerful machine learning algorithm adopted for linear or nonlinear classification, regression, and even outlier detection tasks. SVMs can be adopted for a diversity of tasks, such as text classification, spam detection, handwriting identification, gene expression analysis, face detection, anomaly detection, etc.

Types of Support Vector Machine

• Linear SVM: To divide the data points into distinct classes, linear SVMs employ a linear decision boundary. Linear SVMs are ideal when the data can be properly divided along a linear path. This indicates that the data points may be completely divided into their corresponding classes by a single straight line in two dimensions or a hyperplane in three dimensions.
• Non-Linear SVM: In situations where a straight line cannot divide data into two groups, non-linear SVM can be used to classify the data (in the instance of 2D). Nonlinear SVMs may handle nonlinearly separable data by utilising kernel functions.

Advantages of Support Vector Machine

• The decision function can specify a variety of Kernel functions. Although default kernels are offered, users can optionally define their own custom kernels.
• In situations where there are more dimensions than samples, it is still useful.
• For the decision functions, several kernel functions can be supplied, as well as bespoke kernels.
• Various kernel functions and custom kernels can be provided for the decision functions.

Disadvantages of Support Vector Machine

• When there is more noise in the data set—that is, when target classes overlap—SVM performs poorly.
• The SVM will perform worse when there are more features per data point than there are training data samples.
• When there is more noise in the data set—that is, when target classes overlap—SVM performs poorly.
• There is no probabilistic justification for the classification because the support vector classifier operates by placing data points above and below the classifying hyperplane.

Support Vector Machine for Classification

There are two varieties of SVMs, and each has its own unique behaviour. The linear and non-linear SVMs are these two varieties.

The simplest SVM is linear, and it adheres to a straightforward principle. The linear combination of the input is always identical to the dot product when it is calculated between two characteristics of the input:

f(a, b) = a • b (vector dot product)

This rule does not apply to the non-linear SVM, which is an SVM. The non-linear SVM employs a kernel to calculate the output of the dot product between two input characteristics.

Neural Networks

Neural network is made to replicate the structure and activities of the human brain. Neural networks are elaborate systems made up of linked nodes, or neurons, that work together to solve complex issues. Neural networks, also recognized as deep neural networks or artificial neural networks (ANNs), are a subspace of deep learning technologies that fall under the larger umbrella of artificial intelligence (AI).

Types of neural networks

• Recurrent neural networks (RNNs): RNNs are more complicated in that they save the results of processing nodes and re-input them into the model. The model gains the ability to forecast a layer’s result in this way.
• Deconvolutional neural networks: With deconvolutional neural networks, the CNN model procedure is inverted. They search for missing characteristics or signals that were before thought to be irrelevant to the CNN system’s mission.
• Convolutional neural networks (CNNs): One of the most widely utilised models in use today is the CNN. This computational model has one or more convolutional layers that can be either pooled or fully linked. It is based on a version of multilayer perceptions.
• Modular neural networks: These comprise many neural networks operating independently of one another. During the calculation process, the networks don’t interact with one another or interfere with one another’s operations.

Advantages of Neural networks

• Real-world input-output linkages may be modelled with the aid of the capacity to learn and model complicated, nonlinear relationships.
• The input variables are not constrained in any way, including their distribution.
• ANNs store data not just in a database but across the whole network. This makes sure that even if a tiny bit of data vanishes from one place, the network as a whole keeps running.
• When a network has gradual corruption, it deteriorates gradually over time as opposed to abruptly when an issue arises.

Disadvantages of Neural networks

• Neural networks depend on hardware as they need processors that can process data in parallel.
• One of the main drawbacks of neural networks is the absence of justification for probing results.
• Results that are inaccurate.It can frequently yield erroneous or partial findings if they are not trained correctly.
• It might be difficult to understand how neural networks classify data or generate predictions due to its black box AI approach.

Neural networks for Classification

In particular, neural networks function differently and don’t require kernels. That is, naturally, excluding convolutional neural networks.

In this application, a neural network (NN) with a single hidden layer and a non-linear activation function is referred to as a neural network for classification. For NNs used in classification, the following are the most popular kinds of non-linear activation functions:

hyperbolic tangent, z = tanh(y)

logistic function, z = σ {y}

softmax, Φ(yi) = {e^{yi}} { {j=1}^{K} e^{yj}}

A linear combination of a feature vector (x) and a weight vector (w) is the input for all these functions. Subsequently, they yield an output consisting of a defined interval, often ranging from -1 to 0.

Training process for SVMs and NN

• One further difference relates to the time required to train the algorithm. SVMs are generally very fast to train, which is a consequence of the point we made in the previous section. The same is however not valid for neural networks.
• As we discussed in our article on the advantages and disadvantages of neural networks, some particularly large NNs require in fact several days, sometimes weeks, in order to be trained. This means that restarting the training and initializing the random weights differently, for example, is possible for SVMs but very expensive for NNs.

Optimization techniques for SVMs and NN

• Another distinction is the algorithm used by SVMs and neural networks to optimise their parameters. Gradient descent is usually used for neural network optimisation since it is the most widely used method.
• Instead, SVM employ a technique known as quadratic programming. The optimisation of a function under linear restrictions on its variables is known as quadratic programming. In practice, sequential minimum optimisation is used to tackle quadratic programming for SVMs.

Use Cases of Support Vector Machines and Neural Networks

• Not Enough Data is Sampled in the Feature Space
• There Is limited time for Training
• Any Accuracy Marginal Gain Is Important

Similarities Between Support Vector Machines and Neural Networks

• The non-linearity is included in both machine learning techniques. With SVMs, this is accomplished by utilising a kernel technique. Alternatively, neural networks use non-linear activation functions to embed non-linearity.
• The same classification task against the same dataset may be handled by both SVMs and NNs. This indicates that there is no justification for choosing one solution over another based on the features of the issue.
• But when given maximum training and computing resources, NNs often perform better than SVMs.
• Consequently, non-linear decision functions can be approximated by both groups of techniques.

Difference between Support Vector Machines and Neural Networks

Support Vector Machines	Neural Networks
Support Vector Machine (SVM) is a powerful machine learning algorithm adopted for linear or nonlinear classification, regression, and even outlier detection tasks.	Neural network is a model inspired by the structure and function of the human brain. It consists of interconnected nodes (neurons) organized in layers. NN learns by adjusting the weights and biases of connections between neurons to minimize the error in predictions.
Large data sets are not a good fit for the SVM algorithm.	Neural networks can handle large datasets more effectively, especially when trained using distributed computing or GPU acceleration, as they can process data in parallel across multiple nodes or processors.
The number of parameters of an SVM increases linearly with the input’s linear rise in size.	The number of parameters of an Neural Networks does not increases linearly with the input’s linear rise in size.
SVMs typically do not store information about the training data once the model is trained. They only retain support vectors, which are the data points closest to the decision boundary.	Neural networks store information about the training data in the weights and biases of connections between neurons. This allows NNs to generalize well to new data and handle missing or corrupted inputs.
SVM allows for the use of different kernel functions (e.g., linear, polynomial, radial basis function) to map input data into higher-dimensional feature spaces, enabling nonlinear classification.	Neural networks can model complex nonlinear relationships between input and output variables without explicitly using kernel functions.

Conclusion

In conclusion Support Vector Machine (SVM) is a powerful machine learning algorithm adopted for linear or nonlinear classification, regression, and even outlier detection tasks. A machine learning (ML) model called a neural network is made to resemble the structure and operations of the human brain. Large data sets are not a good fit for the SVM algorithm.Neural networks depend on hardware as they need processors that can process data in parallel.

FAQs on Support Vector Machines vs Neural Networks

Why SVM is better than neural network?

This is due to the fact that an SVM’s initial decision hyperplane will always be situated between support vectors from several classes.

What is the difference between SVM and deep learning?

SVM is a more straightforward model that performs well with sparse data and small datasets.

Can we use SVM in neural network?

Sometimes, the architecture of a shallow neural network is comparable to that of SVMs.

Does SVM use neural networks?

A hybrid learning technique made up of neural networks and support vector machines (SVMs) is called the Neural Support Vector Machine (NSVM).