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Found 13 papers, 2 papers with code
K-Means Kernel Classifier
The least-squares kernel method uses these representative vectors as a training set for the classification task.
Heuristic Optimal Transport in Branching Networks
Optimal transport aims to learn a mapping of sources to targets by minimizing the cost, which is typically defined as a function of distance.
High-Dimensional Vector Semantics
In this paper we explore the "vector semantics" problem from the perspective of "almost orthogonal" property of high-dimensional random vectors.
On the inherent competition between valid and spurious inductive inferences in Boolean data
This incompleteness of the problem will also generate spurious inferences, which are a serious threat to valid inductive inference rules.
Reservoir Computing on the Hypersphere
Reservoir Computing (RC) refers to a Recurrent Neural Networks (RNNs) framework, frequently used for sequence learning and time series prediction.
Randomized Kernel Methods for Least-Squares Support Vector Machines
The least-squares support vector machine is a frequently used kernel method for non-linear regression and classification tasks.
Additive Feature Hashing
That is, we show that additive feature hashing can be performed directly by adding the hash values and converting them into high-dimensional numerical vectors.
Diffusion Self-Organizing Map on the Hypersphere
We discuss a diffusion based implementation of the self-organizing map on the unit hypersphere.
Sandbox Sample Classification Using Behavioral Indicators of Compromise
Thus, every sample is described by a set of BICs triggered by the sample behavior in the sandbox environment.
Autoencoders as Pattern Filters
We discuss a simple approach to transform autoencoders into "pattern filters".
Reaction Diffusion TAP
The recently introduced Theory of the Adjacent Possible (TAP) is a model of combinatorial innovation aiming to explain the "hockey-stick" upward trend of human technological evolution, where an explosion in the number of produced items with increasing complexity suddenly occurs.
TensorFlow Chaotic Prediction and Blow Up
More specifically, the longer term prediction of the system's chaotic behavior quickly deteriorates and blows up due to the nondeterministic behavior of the TensorFlow library.
ELM Ridge Regression Boosting
We discuss a boosting approach for the Ridge Regression (RR) method, with applications to the Extreme Learning Machine (ELM), and we show that the proposed method significantly improves the classification performance and robustness of ELMs.
