Application of perceptron algorithm in machine learning

Perceptron A machine learning algorithm for supervised learning of various binary sorting tasks.

The perceptron algorithm plays an important role in the calculation of certain input data in business intelligence. It can be regarded as an artificial neuron or neural link. One of the best and most specific types of artificial neural networks, the perceptron model is a supervised learning algorithm for binary classifiers. It can be viewed as a single-layer neural network with four main parameters, including input values, weights and biases, net sum, and activation function.

Types of perceptron algorithms

1. Single-layer perceptron model

One of the simplest types of ANN (artificial neural network) is a feedforward network, which contains a threshold transmission. The main goal of single-layer perceptron models is to analyze linearly separable objects with binary outcomes. However, since single-layer perceptrons can only learn linearly separable patterns, for nonlinearly separable problems, we need more complex multi-layer perceptron models.

2. Multi-layer perceptron model

is mainly similar to the single-layer perceptron model, but has more hidden layers.

The perceptron algorithm learns the weights of input signals to draw linear decision boundaries.

Perceptron Learning Rules

The perceptron learning rule states that the algorithm can automatically learn the optimal weight coefficient and determine whether the neuron fires by multiplying the input feature and the weight.

The perceptron algorithm receives multiple input signals. If the sum of the input signals exceeds the threshold, the signal is output; otherwise, it does not return. In supervised learning and classification, it can be used for sample category prediction.

How does the perceptron algorithm work?

As mentioned earlier, a perceptron is considered a single layer of neural links with four main parameters. The perceptron model first multiplies all input values ​​and their weights, and then adds these values ​​to create a weighted sum. Furthermore, apply this weighted sum to the activation function "f" to get the desired output. This activation function is also called a step function and is represented by "f".

This step function or activation function is crucial to ensure that the output maps between (0,1) or (-1,1). Note that the weight entered represents the strength of the node. Similarly, the input value gives the activation function the ability to move the curve up or down.

Advantages and Disadvantages of the Perceptron Algorithm

Advantages:

The multi-layer perceptron model can solve complex nonlinear problems.

It works for both small and large input data.

Helps us get fast predictions after training.

Help us obtain the same accuracy for large and small data.

Disadvantages:

In the multi-layer perceptron model, the calculation is time-consuming and complex.

It is difficult to predict the degree of influence of the dependent variable on each independent variable.

The functionality of a model depends on the quality of training.

Characteristics of the perceptron model

The following are the characteristics of the perceptron model:

It is a machine learning algorithm that uses supervised learning of binary classifiers.

In Perceptron, the weight coefficient is automatically learned.

Initially, the weights are multiplied with the input features and then decide whether to activate the neuron.

The activation function applies a stepping rule to check whether the function is more important than zero.

Linear decision boundaries are drawn that distinguish two linearly separable classes 1 and -1.

If the sum of all input values ​​is greater than the threshold, there must be an output signal; otherwise, no output will be displayed.

Limitations of the perceptron model

The following are the limitations of the perceptron model:

Due to the hard-edge transfer function, the output of the perceptron can only be a binary number (0 or 1).

It can only be used to classify linearly differentiable sets of input vectors. If the input vector is non-linear, it is not easy to classify it correctly.

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