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Smart pointers can facilitate intentional programming by expressing, in the type, how the memory of the referent of the pointer will be managed. For example, if a C++ function returns a pointer, there is no way to know whether the caller should delete the memory of the referent when the caller is finished with the information.
Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.
A function pointer, also called a subroutine pointer or procedure pointer, is a pointer referencing executable code, rather than data. Dereferencing the function pointer yields the referenced function , which can be invoked and passed arguments just as in a normal function call.
Where "new" is the standard routine in Pascal for allocating memory for a pointer, and "hex" is presumably a routine to print the hexadecimal string describing the value of an integer. This would allow the display of the address of a pointer, something which is not normally permitted. (Pointers cannot be read or written, only assigned.)
A training example of SVM with kernel given by φ((a, b)) = (a, b, a 2 + b 2) Suppose now that we would like to learn a nonlinear classification rule which corresponds to a linear classification rule for the transformed data points φ ( x i ) . {\displaystyle \varphi (\mathbf {x} _{i}).}
Another way to create a function object in C++ is to define a non-explicit conversion function to a function pointer type, a function reference type, or a reference to function pointer type. Assuming the conversion does not discard cv-qualifiers , this allows an object of that type to be used as a function with the same signature as the type it ...
In computer science, pointer analysis, or points-to analysis, is a static code analysis technique that establishes which pointers, or heap references, can point to which variables, or storage locations. It is often a component of more complex analyses such as escape analysis. A closely related technique is shape analysis.
The hyperplane learned in feature space by an SVM is an ellipse in the input space. In machine learning , the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original ...