Search results
Results from the WOW.Com Content Network
MOA is an open-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the graphical user interface (GUI), the command-line, and the Java API.
Unlike partitioning and hierarchical methods, density-based clustering algorithms are able to find clusters of any arbitrary shape, not only spheres. The density-based clustering algorithm uses autonomous machine learning that identifies patterns regarding geographical location and distance to a particular number of neighbors.
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2]
Apache Mahout, a library of scalable machine learning algorithms. [78] Deeplearning4j, an open-source, distributed deep learning framework written for the JVM. [79] Keras, a high level open-source software library for machine learning (works on top of other libraries). [80]
Since 1989 the new algorithms (AC, OCC, PF) for non-parametric modeling of fuzzy objects and SLP for expert systems were developed and investigated. [13] Present stage of GMDH development can be described as blossom out of deep learning neuronets and parallel inductive algorithms
OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
However, APSO will introduce new algorithm parameters, it does not introduce additional design or implementation complexity nonetheless. Besides, through the utilization of a scale-adaptive fitness evaluation mechanism, PSO can efficiently address computationally expensive optimization problems.
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.