To evaluate the maximum number of pages required when a system supports a 16-bit address line and a 1K (1024 Bytes) page size, we can break it down as follows: 16-bit Address Line: A 16-bit address line can address up to 216 locations (bytes). Page Size: Given a page size of 1K, which is equivalentRead more
To evaluate the maximum number of pages required when a system supports a 16-bit address line and a 1K (1024 Bytes) page size, we can break it down as follows:
16-bit Address Line:
A 16-bit address line can address up to 216 locations (bytes).
Page Size:
Given a page size of 1K, which is equivalent to 1024 Bytes (Ref: 1 KB = 1024 Bytes).
Now, calculate the maximum number of pages needed:
Total Addressable Locations= 216= 65536 locations.
Page Size = 1024 Bytes.
To find the total number of pages, divide the total addressable locations by the page size:
Total Pages= Total Addressable Locations/Page Size= 65536/1024= 64 pages.
Hence, in a system with a 16-bit address line and a 1K page size, the maximum number of pages required is 64.
Follow the link below: https://sikshapath.in/question/create-a-class-called-matrix-containing-constructor-that-initializes-the-number-of-rows-and-number-of-columns-of-a-new/
Support vector machines Support vector machines are a type of supervised machine algorithm for learning which is used for classification and regression tasks. Though they are used for both classification and regression, they are mainly used for classification challenges. Support vector machines areRead more
Support vector machines
Support vector machines are a type of supervised machine algorithm for learning which is used for classification and regression tasks. Though they are used for both classification and regression, they are mainly used for classification challenges.
Support vector machines are a tool which best serves the purpose of separating two classes. They are a kernel-based algorithm.
A kernel refers to a function that transforms the input data into a high dimensional space where the question or problem can be solved.
A kernel function can be either linear or non-linear. Kernel methods are a type of class of algorithms for pattern analysis.
The primary function of the kernel is to get data as input and transform them into the required forms of output.
In statistics, “kernel” is the mapping function that calculates and represents values of a 2-dimensional data in a 3-dimensional space format.
A support vector machine uses a kernel trick which transforms the data to a higher dimension and then it tries to find an optimal hyperplane between the outputs possible.
Kernel’s method of analysis of data in support vector machine algorithms using a linear classifier to solve non-linear problems is known as ‘kernel trick’.
Kernels are used in statistics and math, but it is most widely and also most commonly used in support vector machines.
Application of Support Vector Machines
The use of support vector machine algorithms and its examples are used in many technologies which incorporate the use of segregation and distinction.
The real-life applications it range from image classification to face detection, recognition of handwriting and even to bioinformatics.
It allows the classification and categorization of both inductive and transductive models. The support vector machine algorithms make use of training data to segregate different types of documents and flies into different categories.
The segregation done by it is based on the data and score generated by the algorithm and then is compared and contrasted to the initial values provided.
Evaluating the maximum number of pages needed, if a system supports 16 bit address line and 1K page size
To evaluate the maximum number of pages required when a system supports a 16-bit address line and a 1K (1024 Bytes) page size, we can break it down as follows: 16-bit Address Line: A 16-bit address line can address up to 216 locations (bytes). Page Size: Given a page size of 1K, which is equivalentRead more
To evaluate the maximum number of pages required when a system supports a 16-bit address line and a 1K (1024 Bytes) page size, we can break it down as follows:
16-bit Address Line:
Page Size:
Now, calculate the maximum number of pages needed:
Total Addressable Locations= 216= 65536 locations.
Page Size = 1024 Bytes.
To find the total number of pages, divide the total addressable locations by the page size:
Total Pages= Total Addressable Locations/Page Size= 65536/1024= 64 pages.
Hence, in a system with a 16-bit address line and a 1K page size, the maximum number of pages required is 64.
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Support vector machines Support vector machines are a type of supervised machine algorithm for learning which is used for classification and regression tasks. Though they are used for both classification and regression, they are mainly used for classification challenges. Support vector machines areRead more
Support vector machines
Support vector machines are a type of supervised machine algorithm for learning which is used for classification and regression tasks. Though they are used for both classification and regression, they are mainly used for classification challenges.
Support vector machines are a tool which best serves the purpose of separating two classes. They are a kernel-based algorithm.
Application of Support Vector Machines
The use of support vector machine algorithms and its examples are used in many technologies which incorporate the use of segregation and distinction.
The real-life applications it range from image classification to face detection, recognition of handwriting and even to bioinformatics.
It allows the classification and categorization of both inductive and transductive models. The support vector machine algorithms make use of training data to segregate different types of documents and flies into different categories.
The segregation done by it is based on the data and score generated by the algorithm and then is compared and contrasted to the initial values provided.
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