Search Results for "data mining algorithm php"

Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM...

Python Data Science Tutorials is a curated learning repository for common data analysis, machine learning, and natural language processing tasks in Python. It combines external tutorials, courses, reference guides, notebooks, articles, and selected code examples. The collection begins with Python fundamentals and then moves into scientific computing, statistics, NumPy, pandas, data exploration, and visualization. Its machine learning sections cover practical algorithms and libraries,...

Bolt is an open-source research project focused on accelerating machine learning and data mining workloads through efficient vector compression and approximate computation techniques. The core idea behind Bolt is to compress large collections of dense numeric vectors and perform mathematical operations directly on the compressed representations instead of decompressing them first. This approach significantly reduces both memory usage and computational overhead when working with...

This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ...

Leark is a Data Mining library developed in C#.NET. It contains several methods for ranking web documents described with a set of normalized features, and a feature selection algorithm. The methods are based on perceptron and clustering.