Past decade has shown that complex networks should be at Structural adaptation Structural adaptation in NNs the core of any AI system that needs to be of robust use hasn't gained as much attention as other aspects of this technology, in any task of reasonable complexity. It has, however, been as many of these methods involve an additional step unfortunate that over the same period, the field of machine and often don't provide a significant benefit compared to the learning (ML) has been stuck in the twin limiting paradigms added complexity. One subfield in literature, called "neural of static topologies and statistical fine-tuning, attempting architecture search," focuses on optimizing the architecture to make up for the limitations of both of these by using itself explicitly (Liu, Simonyan, and Yang 2018; Shin, brute force, in form of overparameterization and computational Packer, and Song 2018; Baker et al. 2016; Stanley et al. requirements accompanying it. Limitations imposed 2019; Liu et al. 2017; Miikkulainen et al. 2019). Some other by these paradigms also prevent solving the crucial problem works view "structural adaptation" as starting from scratch of "catastrophic forgetting" in continual learning. In this or growth, sometimes referred to as Artificial Embryogenesis work, we first propose a novel method of structural adaptation, (Kowaliw et al. 2014), often using evolutionary algorithms.

Logistic regression is a ubiquitous method for probabilistic classification. However, the effectiveness of logistic regression depends upon careful and relatively computationally expensive tuning, especially for the regularisation hyperparameter, and especially in the context of high-dimensional data. We present a prevalidated ridge regression model that closely matches logistic regression in terms of classification error and log-loss, particularly for high-dimensional data, while being significantly more computationally efficient and having effectively no hyperparameters beyond regularisation. We scale the coefficients of the model so as to minimise log-loss for a set of prevalidated predictions derived from the estimated leave-one-out cross-validation error. This exploits quantities already computed in the course of fitting the ridge regression model in order to find the scaling parameter with nominal additional computational expense.