The popular algorithm used for object detection is Support Vector Machines (SVM). Here's a step-by-step explanation:

1. Support Vector Machines (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems.

2. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is the number of features you have) with the value of each feature being the value of a particular coordinate.

3. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well.

4. In two dimensional space, this hyper-plane is a line dividing a plane into two parts where each class lay on either side.

5. The objective of the SVM algorithm is to find a hyper-plane in an N-dimensional space that distinctly classifies the data points

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The popular algorithm used for object detection is Support Vector Machines (SVM). Here's a step-by-step explanation:

1. Support Vector Machines (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems.

2. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is the number of features you have) with the value of each feature being the value of a particular coordinate.

3. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well.

4. In two dimensional space, this hyper-plane is a line dividing a plane into two parts where each class lay on either side.

5. The objective of the SVM algorithm is to find a hyper-plane in an N-dimensional space that distinctly classifies the data points

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The popular algorithm used for object detection is Support Vector Machines (SVM). Here's a step-by-step explanation:

1. Support Vector Machines (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems.

2. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is the number of features you have) with the value of each feature being the value of a particular coordinate.

3. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well.

4. In two dimensional space, this hyper-plane is a line dividing a plane into two parts where each class lay on either side.

5. The objective of the SVM algorithm is to find a hyper-plane in an N-dimensional space that distinctly classifies the data points

Which of the following is a popular algorithm used for object detection?Review LaterSupport Vector Machines (SVM)K-means clusteringApriori algorithmDepth-first search

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