1 code implementation • 23 Dec 2020 • M. Andrecut
The least-squares kernel method uses these representative vectors as a training set for the classification task.
1 code implementation • 11 Nov 2023 • M. Andrecut
Optimal transport aims to learn a mapping of sources to targets by minimizing the cost, which is typically defined as a function of distance.
no code implementations • 23 Feb 2018 • M. Andrecut
In this paper we explore the "vector semantics" problem from the perspective of "almost orthogonal" property of high-dimensional random vectors.
no code implementations • 6 Jan 2018 • M. Andrecut
This incompleteness of the problem will also generate spurious inferences, which are a serious threat to valid inductive inference rules.
no code implementations • 24 Jun 2017 • M. Andrecut
Reservoir Computing (RC) refers to a Recurrent Neural Networks (RNNs) framework, frequently used for sequence learning and time series prediction.
no code implementations • 22 Mar 2017 • M. Andrecut
The least-squares support vector machine is a frequently used kernel method for non-linear regression and classification tasks.
no code implementations • 7 Feb 2021 • M. Andrecut
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.
no code implementations • 31 May 2021 • M. Andrecut
We discuss a diffusion based implementation of the self-organizing map on the unit hypersphere.
no code implementations • 18 Jan 2022 • M. Andrecut
Thus, every sample is described by a set of BICs triggered by the sample behavior in the sandbox environment.
no code implementations • 26 Feb 2023 • M. Andrecut
We discuss a simple approach to transform autoencoders into "pattern filters".
no code implementations • 25 Jul 2023 • M. Andrecut
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.
no code implementations • 14 Sep 2023 • M. Andrecut
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.
no code implementations • 24 Oct 2023 • M. Andrecut
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.