Question

A fruit dealer has a warehouse to store oranges and bananas. Initially, they are stored together in the same container. To save packaging time and cost, the dealer wants to install an intelligent machine that can classify the fruits into oranges and bananas automatically. The fruits are loaded on a conveyor that passes through the machine, which is equipped with sensors to measure shape, texture, and weight of the fruits. For simplicity, let us assume the sensors output binary values as follows. 1) Shape: 1 if round; -1 if elliptical 2) Texture: 1 if smooth; -1 if rough 3) Weight: 1 if more than 1 lb; -1 otherwise. Each fruit is represented by a vector of [Shape Texture Weight]. For training the classifier, the following instances are provided: [1 -1 -2] for orange (+1) and (-1 1 -1] for banana (-1). 1)

Train a perceptron (NOT a MLP) with sign activation function to identify oranges or bananas using the two instances. Set initial weights W = [0.5 0.5 0.5]. Threshold is fixed at 0.5 (or bias at -0.5) and won't be changed. The learning rate is fixed at 0.5. 2)

Answer 1

No training of the perceptron is necessary as it already correctly classifies the given instances of oranges and bananas using the initial weights and threshold provided.

Step-by-step explanation:

The student's question pertains to the training of a perceptron, a type of artificial neural network, using provided instances for classifying oranges and bananas. The perceptron uses a sign activation function and has been initialized with weights and a fixed threshold (bias). To train the perceptron, one must adjust the weights based on the predefined learning rate when the output does not match the expected result. Since the provided instances already output the correct classification (an orange is classified correctly with a positive sign and a banana with a negative sign using the initial weights and threshold), in this case, no training is necessary as the perceptron is already performing correctly.

The subject of this question is the implementation of an intelligent machine that can classify fruits based on their characteristics. In this case, the machine uses sensors to measure the shape, texture, and weight of the fruits and outputs binary values. The goal is to train a perceptron with sign activation function to identify oranges and bananas. The training instances provided are vectors representing the fruits' attributes.

The answer to the question is: Train a perceptron with sign activation function to identify oranges or bananas using the two instances. The weights are initialized as W = [0.5 0.5 0.5] and the threshold is fixed at 0.5. The learning rate is also fixed at 0.5.