K-means is the most primitive and easy to use clustering algorithm (also a Machine Learning algorithm).
There are 4 basic steps of K-means:
- Choose K different initial data points on instance space (as initial centroids) - centroid is the mean points of the clusters that overview the attributes of the classes-.
- Assign each object to the nearest centroid.
- After all the object are assigned, recalculate the centroids by taking the averages of the current classes (clusters)
- Do 2-3 until centroid are stabilized.
Caveats for K-means:
- Although it can be proved that the procedure will always terminate, the k-means algorithm does not necessarily find the most optimal configuration, corresponding to the global objective function minimum.
- The algorithm is also significantly sensitive to the initial randomly selected cluster centres. The k-means algorithm can be run multiple times to reduce this effect.
Here is the basic animation to show the intuition of K-means.
-Convexity, including convex optimization and formulation of problems as convex programs. Two important subsets of this are linear programming and proximal gradient-style optimization algorithms and formulations, which have a ridiculously vast array of applications for industrial engineering and machine learning.
-Probabilistic modeling and inference: Graphical models and max-entropy models are the most important, and have a vast array of applications in machine learning and more structured statistical modeling. Markov Chain Monte Carlo is a terrific and amazing algorithm with a great special case called Gibbs sampling - they both present almost generic methods of Continue reading What are the needs of Machine Learning?
The most basic algorithm is Gradient Descent to optimize your cost function in a machine learning algorithm but it has some problems like;
- Choosing alpha value for the steps of convergence process.
- Too much iteration for converge.
There are some other approaches that are;
- Conjugate Gradient
As a headline note, I am not writing these staff to give all the details and the information about the headings, also I am not qualified as this much. I just trying to underline some basic facts for the ones who are interested in machine learning and searching some facts to investigate. Thus my headings just small introduction for your ML world search.
Linear Regression: a basic algorithm to estimate continuous output value by considering the attributes of the given instance according to the given instances in data-set with their attribut Continue reading Machine Learning Terms (#3)
First of all we need to see How a ML algorithm is working. Here is the schema.
A ML process takes these steps, Continue reading Process of defining a machine learning solution (ML#2)
If you are working on some project related to machine learning (ML) or you are a newbie researcher knowing these terms and definitions might be useful.
Machine Learning: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E -- Tom M. Mitchell Continue reading Some Basic Machine Learning Terms #1
This is a good visualization for what SVM does with kernel function.To be a brief information. Support Vector Machine is a good algorithm for binary classification* and can be used with different kernel functions* related to your purpose of learning. Continue reading What SVM does with a video