Let’s say you have a bunch of datapoints and you want to come up with a nice model for them. We want this model to satisfy all the points in the best possible way. If we do this, then we will be able to use a mathematical formula to extract information about unknown points. At the same time, we should make sure that we don’t overfit our model to these datapoints. If we overfit our model, then it will tune itself too much to our datapoints and perform poorly on unknown data. So how we pick the best model? Where do we draw the line? Continue reading

# Tag Archives: Bayesian

# Bayes Point Machines

In machine learning, we use a lot of supervised learning models to analyze data and recognize patterns. If we consider the basic problem of binary classification, a machine learning algorithm takes a set of input data and predicts which of two possible classes a particular input belongs to. Kernel-classifiers comprise a powerful class of non-linear decision functions for binary classification. These classifiers are very useful when you cannot draw a straight line to separate two classes. The support vector machine (SVM) is a good example of a learning algorithm for kernel classifiers. It looks at all the boundaries and picks the one with the largest margin. It is widely used in many different fields and it has a very strong mathematical foundation. Now it is being claimed that Bayes Point Machine (BPM) is an improvement over SVM. Pretty bold claim! So what exactly is a BPM? How is it better than SVM? Continue reading

# Bayesian Classifier

In machine learning, classification is the process of identifying the category of an unknown input based on the set of categories we already have. A classifier, as the name suggests, classifies things into multiple categories. It is used in various real life situations like face detection, image search, fingerprint recognition, etc. Some of the tasks are really simple and a machine can identify the class with absolute certainty. A common example would be to determine if a given number is even or odd. Pretty simple right! But most of the real life problems are not this simple and there is absolutely no way a machine can identify it with absolute certainty. For example, object recognition, weather prediction, handwriting analysis etc. So how do machines deal with these problems? What approach can be used here? Continue reading