Support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. (Wikipedia)
Estimation of the prerequisites for the maintenance, repair, rehabilitation and reconstruction of pavement is one of the requirements for the design and maintenance of the structure of pavement. The pavement design methods are based on providing a proper prediction of the structure of pavement to keep it in permissible condition. The term'remaining service life' (RSL) refers to the time it takes for the pavement to reach an unacceptable status and need to be rehabilitated or reconstructed (Elkins, Thompson, Groerger, Visintine, & Rada, 2013 Elkins, G. E., Thompson, T. M., Groerger, J. L., Visintine, B., & Rada, G. R. (2013). Prediction of the RSL is a basic concept of pavement maintenance planning. Awareness of the future conditions of pavement is a key point in making decisions in the planning of pavement maintenance.
Which outputs the following: the function call, SVM type, kernel and cost (which is set to its default). In case you are wondering about gamma, although it's set to 0.5 here, it plays no role in linear SVMs. We'll say more about it in the sequel to this article in which we'll cover more complex kernels. More interesting are the support vectors. In a nutshell, these are training dataset points that specify the location of the decision boundary. We can develop a better understanding of their role by visualising them. To do this, we need to know their coordinates and indices (position within the dataset). This information is stored in the SVM model object.
Unraveling The Dream Within The Dream! Very few would need a hint to guess that the picture on the left is taken from the movie, Inception. The behavior of the spinning top helps in differentiating reality from illusion. It's a mesmerizing concept attempting to visually articulate the subconscious mind. Inception is a movie based on lucid dreaming. The science fiction shows how something that cannot be achieved in real world, can be achieved by transforming the world to a virtual reality and then after the goal is achieved, transform the world back to reality.
Support vector machines (SVMs) with sparsity-inducing nonconvex penalties have received considerable attentions for the characteristics of automatic classification and variable selection. However, it is quite challenging to solve the nonconvex penalized SVMs due to their nondifferentiability, nonsmoothness and nonconvexity. In this paper, we propose an efficient ADMM-based algorithm to the nonconvex penalized SVMs. The proposed algorithm covers a large class of commonly used nonconvex regularization terms including the smooth clipped absolute deviation (SCAD) penalty, minimax concave penalty (MCP), log-sum penalty (LSP) and capped-$\ell_1$ penalty. The computational complexity analysis shows that the proposed algorithm enjoys low computational cost. Moreover, the convergence of the proposed algorithm is guaranteed. Extensive experimental evaluations on five benchmark datasets demonstrate the superior performance of the proposed algorithm to other three state-of-the-art approaches.
SVM is a powerful technique and especially useful for data whose distribution is unknown (also known as non-regularity in data). Because the example considered here consisted of only two features, the SVM fitted by R here is also known as linear SVM. SVM is powered by a kernel for dealing with various kinds of data and its kernel can also be set during model tuning. Some such examples include gaussian and radial. Hence, SVM can also be used for non-linear data and does not require any assumptions about its functional form.
To separate the two classes of data points, there are many possible hyperplanes that could be chosen. Our objective is to find a plane that has the maximum margin, i.e the maximum distance between data points of both classes. Maximizing the margin distance provides some reinforcement so that future data points can be classified with more confidence. Hyperplanes are decision boundaries that help classify the data points. Data points falling on either side of the hyperplane can be attributed to different classes.
This month we are delighted to have Professor Paul Walsh from CIT speaking at Cork AI. The talk will introduce Support vector machines (SVMs), which are supervised machine learning algorithms that are widely used for a range of real word problems. Key terms and concepts will be described and it will be shown how SVM algorithms can build linear and complex models that can accurately classify unseen data. In order to get the best machine learning performance, the tuning and evaluation of SVMs will also be demonstrated. Live demos and hands on coding opportunities will be provided and a real-world application will be show-cased.
Iris, the part of the eye responsible for controlling the amount of light entering into it, has been a subject of psychological interest for centuries. Progressing from physiology, literature and poetry, eyes are being used in neuro-linguistic programming (NLP), which focuses on interactions of the human body, iris movements and positions. Basically, NLP is being used to gain focus on assessing human behaviour and mental activities. Lately, machine learning has also made its way into psychology-related issues.
This is tough for five-year-olds, but I'll give it a shot for ten-year-olds. Like a lot of other machine learning algorithms, SVMs take some data to start with that's already classified (the training set), and tries to predict a set of unclassified data (the testing set). The data that we have often has a lot of different features, and so we can end up plotting each data item as a point in space, with the value of each feature being the value at a particular coordinate. Now (for two data features) what we want to do is find some line that splits the data between the two differently classified groups of data as well as we can. This will be the line such that the distances from the closest point in each of the two groups will be farthest away.
In this blog, I will show you how to implement a trading strategy using the regime predictions made in the previous blog. Do read it, there is a special discount for you at the end of this. There is one thing that you should keep in mind before you read this blog though: The algorithm is just for demonstration and should not be used for real trading without proper optimization. First, I imported the necessary libraries. If you do not have this package, I suggest you install it first or change your data source to google.