Search results
Results From The WOW.Com Content Network
Machine learning techniques used for content generation include Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN), Generative Adversarial networks (GAN), and K-means clustering. Not all of these techniques make use of ANNs, but the rapid development of deep learning has greatly increased the potential of techniques that do. [29]
In others words, the sample complexity (,,) defines the rate of consistency of the algorithm: given a desired accuracy and confidence , one needs to sample (,,) data points to guarantee that the risk of the output function is within of the best possible, with probability at least .
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.
The learning problem with the least squares loss function and Tikhonov regularization can be solved analytically. Written in matrix form, the optimal w {\displaystyle w} is the one for which the gradient of the loss function with respect to w {\displaystyle w} is 0.
Model order reduction aims to lower the computational complexity of such problems, for example, in simulations of large-scale dynamical systems and control systems. By a reduction of the model's associated state space dimension or degrees of freedom , an approximation to the original model is computed which is commonly referred to as a reduced ...
It is impossible to count the number of steps of an algorithm on all possible inputs. As the complexity generally increases with the size of the input, the complexity is typically expressed as a function of the size n (in bits) of the input, and therefore, the complexity is a function of n. However, the complexity of an algorithm may vary ...
Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models , especially in processing long sequences.