As you may have read elsewhere on this site, HH can result in your body having too much iron.
In the following sections I will provide an intuitive explanation of this concept, illustrated by a clear example of overfitting due to the curse of dimensionality. Consider an example in which we have a set of images, each of which depicts either a cat or a dog. We would like to create a classifier that is able to distinguish dogs from cats automatically.
To do so, we first need to think about a descriptor for each object class that can be expressed by numbers, such that a mathematical algorithm, i. We could for instance argue that cats and dogs generally differ in color.
A possible descriptor that discriminates these two classes could then consist of three number; the average red color, the average green color and the average blue color of the image under consideration.
A simple linear classifier for instance, could combine these features linearly to decide on the class label: Therefore, we could decide to add some features that describe the texture of the image, for instance by calculating the average edge or gradient intensity in both the X and Y direction.
We now have 5 features that, in combination, could possibly be used by a classification algorithm to distinguish cats from dogs. To obtain an even more accurate classification, we could add more features, based on Sample answer the curse or texture histograms, statistical moments, etc.
Maybe we can obtain a perfect classification by carefully defining a few hundred of these features?
The answer to this question might sound a bit counter-intuitive: In fact, after a certain point, increasing the dimensionality of the problem by adding new features would actually degrade the performance of our classifier. Further increasing the dimensionality without increasing the number of training samples results in a decrease in classifier performance.
In the next sections we will review why the above is true, and how the curse of dimensionality can be avoided. However, due to our limited time and processing power, we were only able to obtain 10 pictures of cats and dogs. The end-goal in classification is then to train a classifier based on these 10 training instances, that is able to correctly classify the infinite number of dog and cat instances which we do not have any information about.
We can start by a single feature, e. A single feature does not result in a perfect separation of our training data.
Figure 2 shows that we do not obtain a perfect classification result if only a single feature is used. Therefore, we might decide to add another feature, e. Adding a second feature still does not result in a linearly separable classification problem: No single line can separate all cats from all dogs in this example.
Finally we decide to add a third feature, e. Adding a third feature results in a linearly separable classification problem in our example. A plane exists that perfectly separates dogs from cats. In the three-dimensional feature space, we can now find a plane that perfectly separates dogs from cats.
This means that a linear combination of the three features can be used to obtain perfect classification results on our training data of 10 images: The more features we use, the higher the likelihood that we can successfully separate the classes perfectly.
The above illustrations might seem to suggest that increasing the number of features until perfect classification results are obtained is the best way to train a classifier, whereas in the introduction, illustrated by figure 1, we argued that this is not the case.
However, note how the density of the training samples decreased exponentially when we increased the dimensionality of the problem. In the 1D case figure 210 training instances covered the complete 1D feature space, the width of which was 5 unit intervals. If we would keep adding features, the dimensionality of the feature space grows, and becomes sparser and sparser.
Due to this sparsity, it becomes much more easy to find a separable hyperplane because the likelihood that a training sample lies on the wrong side of the best hyperplane becomes infinitely small when the number of features becomes infinitely large.
However, if we project the highly dimensional classification result back to a lower dimensional space, a serious problem associated with this approach becomes evident: Using too many features results in overfitting.
The classifier starts learning exceptions that are specific to the training data and do not generalize well when new data is encountered. Figure 6 shows the 3D classification results, projected onto a 2D feature space.
Whereas the data was linearly separable in the 3D space, this is not the case in a lower dimensional feature space. In fact, adding the third dimension to obtain perfect classification results, simply corresponds to using a complicated non-linear classifier in the lower dimensional feature space.
As a result, the classifier learns the appearance of specific instances and exceptions of our training dataset.Ask questions and get answers from people sharing their experience with Curse.
Danse Macabre: Curse of the Banshee Collector's Edition for iPad, iPhone, Android, Mac & PC! Can you uncover who’s after your dance troupe?! THE QUESTIONS!!! What is the main theme of the story in the novel you have studied?
Give reasons and evidence from the novel to support your answer. Using the details of. Shadow Wolf Mysteries: Curse of the Full Moon for iPad, iPhone, Android, Mac & PC!
Called into a small city from the capitol, you have been put in charge of solving a string of mysterious murders!! In this article, we discuss the so called 'Curse of Dimensionality' and overfitting as a result, and explain why it is important when designing a classifier.
a) In the Curse by Lee Sue Ann, the character whom I think is exemplary is The Old Lady of the forest. Throughout the novel, she shows strength of character and wisdom.
When she first moves to the village as a newlywed, she is popular among the villagers and she enjoys a blissful marital life with her husband.