Accelerated learning on the connection machine
نویسندگان
چکیده
The complexity of most machine learning techniques can be improved by transforming iterative components into their parallel equivalent. Although this parallelization has been considered in theory, few implementations have been performed on existing parallel machines. The parallel architecture of the Connection Machine provides a platform for the implementation and evaluation of parallel learning techniques. The architecture of the Connection Machine is described along with limitations of the language interface that constrain the implementation of learning programs. Connection Machine implementations of two learning programs, Perceptron and AQ, are described , and their computational complexity is compared to that of the corresponding sequential versions using actual runs on the Connection Machine. Techniques for parallelizing ID3 are also analyzed, and the advantages and disadvantages of parallel implementation on the Connection Machine are discussed in the context of machine learning.
منابع مشابه
Machine learning algorithms in air quality modeling
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...
متن کاملComparative Analysis of Machine Learning Algorithms with Optimization Purposes
The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches. Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data. In this paper, a methodology has been employed to opt...
متن کاملMachine learning algorithms for time series in financial markets
This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...
متن کاملA Hybrid Machine Learning Method for Intrusion Detection
Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implemen...
متن کاملThe Physical Systems Behind Optimization Algorithms
We use differential equations based approaches to provide some physics insights into analyzing the dynamics of popular optimization algorithms in machine learning. In particular, we study gradient descent, proximal gradient descent, coordinate gradient descent, proximal coordinate gradient, and Newton’s methods as well as their Nesterov’s accelerated variants in a unified framework motivated by...
متن کامل