Scalable machine learning
Scalable machine learning can be defined as a class of algorithms that can handle the data sets. Scalable algorithms can perform the accurate computation of huge data-sets and also consume less amount of storage space. Big data scalability also has the similar functionality that provides a platform to scale the massive data in a single application.
Scalable and large scale are the terms used in machine learning and this word usage was initiated from the big data. Certain difficulties have been faced while handling the huge amount of data. Identifying the learning algorithms and data analysis algorithms are the problems arisen with the data sets.
Scalability issue is not often solved. Multicore processors being a big reason behind the problem could not be solved. Scalable machine learning is based on identifying efficient algorithms and frequent approximations to the original algorithms that will compute the data more effectively.
Scalable for logistic-normal models
Gibbs sampling algorithm describes the correct distribution through ideas of data augmentation. This algorithmic approach is used to figure out the graphical models. This estimation algorithm is focused on the graphical models.
Scalable Approach to Probabilistic Latent Space Inference of Large-scale Network
A stochastic variational algorithm based on both estimation and approximation. This algorithm allows analyzing the RealNetworks connected with many vertices. This can be achieved within hours and this algorithm is very efficient in analyzing the networks.
Scalable kernels for graphs with continuous attributes
This algorithm has a number of data points with the squared run-time. Path kernels with complete complexity. This algorithm cannot scale well than the other could perform.
The algorithmic side could get the better outcome. If there is still scope of scalability but, the algorithm can result in a better outcome than the scalable can do.
Stochastic gradient descent
Stochastic gradient descent is a machine learning algorithm and these algorithms are used to improve the linear SVM classifiers and logistic regression and identify the error points to compute.
A prediction error will be computed and also provide the information to reduce the size of the error. These sort of stochastic gradient descent algorithms need to store the model parameters.
Data can be scaled using Hadoop and spark technologies. These technologies are enough to extract the feature to apply the desired operation to every point in the data sets. Parallelization is also a beneficial case that can be achieved by applying cross-validation. Many models with different parameters sets enable the efficient set to be executed by cross-validation.
Scalability differs with technologies, in case of Big data computation is clearly defined and also raised questions to parallelize the data. In machine learning, it has totally relied on the model and its performance. Due to the data, clattering methods can be implemented with liberty.