Abstract—Clustering is an easy to use and implement method of unsupervised inductive inference. Clustering can be used to learn discrete or continuous valued hypotheses and create compact groups of objects that display similar characteristics, while maintaining a high degree of separation from other groupings. This paper is a survey of some of the methods of [...]
Posts Tagged ‘machine learning’
K Nearest Neighbors in Java
In a previous post, I explored how one might apply classification to solve a complex problem. This post will explore the code necessary to implement that nearest neighbor classification algorithm. If you would like a full copy of the source code, it is available here in zip format. Knn.java – This is the main driver [...]
Content Recommendation Systems for a Mobile Platform
Abstract—Recommendation systems take artifacts about items and provide suggestions to the user on what other products the might like. There are many different types of recommender algorithms, including nearest-neighbor, linear classifiers and SVMs. However, most recommender systems are collaborative systems that rely on users to rate the products that they bought. This paper presents a [...]
ID3 Decision Trees in Java
In a previous post, I explored how one might apply decision trees to solve a complex problem. This post will explore the code necessary to implement that decision tree. If you would like a full copy of the source code, it is available here in zip format. Entropy.java – In Entropy.java, we are concerned with [...]
Decision Tree Learning Acting As A Cardiologist
Abstract—Decision trees are one of the most widely used methods of inductive inference. They can be used to learn discrete or continuous valued hypotheses and create compact rules for evaluation of a set of data. An advantage of decision and regression trees is that they are robust to noisy data, which makes them perfectly suited [...]