SVM - Revision history

Mcintyre at 19:40, 22 May 2023

2023-05-22T19:40:47Z

Mcintyre at 19:38, 22 May 2023

2023-05-22T19:38:38Z

Mcintyre at 19:37, 22 May 2023

2023-05-22T19:37:36Z

Mcintyre at 19:30, 22 May 2023

2023-05-22T19:30:40Z

Mcintyre at 19:23, 22 May 2023

2023-05-22T19:23:41Z

Mcintyre at 19:18, 22 May 2023

2023-05-22T19:18:44Z

Mcintyre at 19:12, 22 May 2023

2023-05-22T19:12:14Z

Mcintyre at 19:06, 22 May 2023

2023-05-22T19:06:58Z

Mcintyre at 18:58, 22 May 2023

2023-05-22T18:58:17Z

Mcintyre at 18:56, 22 May 2023

2023-05-22T18:56:50Z

@@ Line 3: / Line 3: @@
-<u><b>The following are some useful YouTube videos:</b></u> <br>
+<u><b>The following are some useful YouTube videos:</b></u>
 <html>
    <head>
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    </body>
 </html>
 === <u>Part 1</u> ===
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 </html>
 [https://www.youtube.com/watch?v=efR1C6CvhmE Support Vector Machines Main Ideas] <br>
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 </html>
 [https://www.youtube.com/watch?v=Toet3EiSFcM The Polynomial Kernel] <br>

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    <body>
      <div id="video-container">
      </div>

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    <body>
      <div id="video-container">
-       <button onclick="loadVideo('efR1C6CvhmE')">SVM: Main Ideas</button>
+       <button onclick="loadVideo('efR1C6CvhmE')">SVM: Main Ideas</button> <font size="1" color="blue">&larr; Click-to-play</font>
      </div>
    </body>
 </html>
@@ Line 72: / Line 57: @@
    <body>
      <div id="video-container">
-       <button onclick="loadVideo('Toet3EiSFcM')">SVM: The Polynomial Kernel</button>
+       <button onclick="loadVideo('Toet3EiSFcM')">SVM: The Polynomial Kernel</button> <font size="1" color="blue">&larr; Click-to-play</font>
      </div>
@@ Line 103: / Line 88: @@
    <body>
      <div id="video-container">
-       <button onclick="loadVideo('Qc5IyLW_hns')">SVM: The Radial (RBF) Kernel</button>
+       <button onclick="loadVideo('Qc5IyLW_hns')">SVM: The Radial (RBF) Kernel</button> <font size="1" color="blue">&larr; Click-to-play</font>
      </div>

@@ Line 3: / Line 3: @@
-<u><b>The following are some useful YouTube videos:</b></u>
+<u><b>The following are some useful YouTube videos:</b></u> <br>
 === <u>Part 1</u> ===
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          var iframe = document.createElement('iframe');
          iframe.src = videoUrl;
-         iframe.width = 400;
+         iframe.width = 960;
-         iframe.height = 240;
+         iframe.height = 540;
          iframe.setAttribute('allow', 'autoplay');
          container.innerHTML = ''; // Clear existing content
@@ Line 54: / Line 82: @@
          var iframe = document.createElement('iframe');
          iframe.src = videoUrl;
-         iframe.width = 400;
+         iframe.width = 960;
-         iframe.height = 240;
+         iframe.height = 540;
          iframe.setAttribute('allow', 'autoplay');
          container.innerHTML = ''; // Clear existing content
@@ Line 85: / Line 113: @@
          var iframe = document.createElement('iframe');
          iframe.src = videoUrl;
-         iframe.width = 400;
+         iframe.width = 960;
-         iframe.height = 240;
+         iframe.height = 540;
          iframe.setAttribute('allow', 'autoplay');
          container.innerHTML = ''; // Clear existing content

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 == Support Vector Machines ==
 <font>Support Vector Machines (SVM) are a class of machine learning algorithms used for classification and regression tasks. In a classification problem, SVMs aim to find an optimal hyperplane that separates different classes of data points with the largest possible margin. The hyperplane is a decision boundary that maximizes the distance between the nearest data points from each class, known as support vectors. SVMs can handle both linearly separable and non-linearly separable data by employing various kernel functions. The idea behind SVMs is to transform input data into a higher-dimensional feature space using the kernel trick, which allows the algorithm to find a linear separation in this transformed space. This transformation enables SVMs to handle complex decision boundaries that may not be linear in the original feature space. During the training phase, SVMs aim to find the optimal hyperplane by solving a quadratic optimization problem. The objective is to minimize the classification error while maximizing the margin. The margin is defined as the perpendicular distance between the hyperplane and the support vectors. By maximizing the margin, SVMs tend to produce a more robust and generalized model. In addition to binary classification, SVMs can be extended to handle multi-class problems using techniques such as one-vs-one or one-vs-all classification. SVMs also have a formulation for regression tasks, known as Support Vector Regression (SVR), where the objective is to fit the data while limiting the deviations within a certain margin. SVMs have several advantages, such as their ability to handle high-dimensional data, effective generalization, and resistance to overfitting. They can be computationally expensive, particularly with large datasets, and the selection of the appropriate kernel and its parameters can be challenging.</font>
 <u><b>The following are some useful YouTube videos:</b></u>
-<script>
+=== <u>Part 1</u> ===
-   function loadVideo(videoUrl) {
+<font><i>A run down of what SVMs are and what they can be used for. All of the necessary background to get started with using SVMs.</i></font> <br>
-     var container = document.getElementById('video-container');
+<html>
-    container.innerHTML = ''; // Clear existing content
+  <head>
-     var iframe = document.createElement('iframe');
+     <script>
-    iframe.src = videoUrl;
+      function loadVideo(videoId) {
-    iframe.width = 400;
+        var container = document.getElementById('video-container');
-    iframe.height = 240;
+        var videoUrl = 'https://www.youtube.com/embed/' + videoId;
-=== <u>Part 1</u> ===
+        var iframe = document.createElement('iframe');
-<font><i>A run down of what SVMs are and what they can be used for. All of the necessary background to get started with using SVMs.</i></font> <br>
+        iframe.src = videoUrl;
-<button onclick="loadVideo('https://www.youtube.com/embed/efR1C6CvhmE')">SVM: Main Ideas</button> <br>
+        iframe.width = 400;
 <br>
 === <u>Part 2</u> ===
 <font><i>A mathematical description of the Polynomial Kernel and how SVMs use this type of kernel to classify data. This video describes how the Polynomial Kernel classifies data in a set number of dimensions.</i></font> <br>
-<button onclick="loadVideo('https://www.youtube.com/embed/Toet3EiSFcM')">SVM: The Polynomial Kernel</button> <br>
+<html>
 <br>
 === <u>Part 3</u> ===
 <font><i>A mathematical description of the Radial Kernel and how SVMs use this type of kernel to classify data. This video describes how the RBF classifies data in infinite dimensions.</i></font> <br>
-<button onclick="loadVideo('https://www.youtube.com/embed/Qc5IyLW_hns')">SVM: The Radial (RBF) Kernel</button> <br>
+<html>
 <br>

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-<!DOCTYPE html>
+== Support Vector Machines ==
 <html>
    <head>
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    <body>
      <div id="video-container">
-       <button onclick="loadVideo()">Click to Play Video</button>
+       <button onclick="loadVideo()">SVM: Main Ideas</button>
      </div>
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    </body>
 </html>

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 <html>
    <head>
-     <title>YouTube Video Auto Play</title>
+     <title>YouTube Video Click-to-Play</title>
    </head>
    <body>
-     <div id="video-container"></div>
+     <div id="video-container">
      <script>
        function loadVideo() {
          var container = document.getElementById('video-container');
-         var videoUrl = 'https://www.youtube.com/embed/efR1C6CvhmE?autoplay=1';
+         var videoUrl = 'https://www.youtube.com/embed/efR1C6CvhmE';
          var iframe = document.createElement('iframe');
@@ Line 17: / Line 19: @@
          iframe.height = 240;
          iframe.setAttribute('allow', 'autoplay');
          container.appendChild(iframe);
+       }
      </script>
    </body>
 </html>