Answer:To simplify the discussion, we will only consider two-class classifiers in this section and define a linear classifier as a two-class classifier that decides class membership by comparing a linear combination of the features to a threshold.
Figure 14.8: There are an infinite number of hyperplanes that separate two linearly separable classes.
\includegraphics[width=6cm]{vclassline.eps}
In two dimensions, a linear classifier is a line. Five examples are shown in Figure 14.8 . These lines have the functional form $w_1x_1+w_2x_2=b$. The classification rule of a linear classifier is to assign a document to $c$ if $w_1x_1+w_2x_2>b$ and to $\overline{c}$ if $w_1x_1+w_2x_2\leq b$. Here, $(x_1, x_2)^{T}$ is the two-dimensional vector representation of the document and $(w_1, w_2)^{T}$ is the parameter vector that defines (together with $b$) the decision boundary. An alternative geometric interpretation of a linear classifier is provided in Figure 15.7 (page [*]).
We can generalize this 2D linear classifier to higher dimensions by defining a hyperplane as we did in Equation 140, repeated here as Equation 144:
\begin{displaymath}
\vec{w}^{T}\vec{x} = b
\end{displaymath} (144)
The assignment criterion then is: assign to $c$ if $\vec{w}^{T}\vec{x} > b$ and to $\overline{c}$ if $\vec{w}^{T}\vec{x} \leq b$. We call a hyperplane that we use as a linear classifier a decision hyperplane .
Figure 14.9: Linear classification algorithm.
\begin{figure}\begin{algorithm}{ApplyLinearClassifier}{\vec{w},b,\vec{x}}
score ...
...in{IF}{score>b}
\RETURN{1}
\ELSE
\RETURN{0}
\end{IF}\end{algorithm}
\end{figure}
The corresponding algorithm for linear classification in $M$ dimensions is shown in Figure 14.9 . Linear classification at first seems trivial given the simplicity of this algorithm. However, the difficulty is in training the linear classifier, that is, in determining the parameters $\vec{w}$ and $b$ based on the training set.
Explanation:
Answer:
Required code is given below:
Explanation:
monitor bounded buffer {
int items[MAX ITEMS];
int numItems = 0;
condition full, empty;
void produce(int v)
{
while (numItems == MAX ITEMS) full.wait();
items[numItems++] = v;
empty.signal();
}
int consume()
{
int retVal;
while (numItems == 0) empty.wait();
retVal = items[--numItems];
full.signal();
return retVal;
}
}
Internet Explorer 9+ is the web browser recommended to use with recorders.
<h3>What is a website?</h3>
A website is a collection of web pages and related material that is published on at least one server and given a shared domain name.
As we know,
Recorders are perfect for desktop applications because they can record a variety of items, including mouse clicks, scrolling, radio buttons, list boxes, checkboxes, and drop-down menus.
Thus, Internet Explorer 9+ is the web browser recommended to use with recorders if Ginny faced an application error while executing the recorder in opera.
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No, I am not in active recovery from a substance use disorder (addiction). Active recovery is defined as being free from an alcohol or other drug use disorder or no longer using alcohol or other drugs is a true statement.
<h3>What does it mean to be in addiction recovery?</h3>
The statement implies that a person is working very hard to be successful in handling their addiction and getting back control of your life.
Note that it is not an easy road but one can overcome. Therefore, my response is No, I am not in active recovery from a substance use disorder (addiction) because i do not drink it. Active recovery is defined as being free from an alcohol or other drug use disorder or no longer using alcohol or other drugs is a true statement.
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