1 –Independent Work Report, Spring 2015– Synaptic Pruning Mechanisms in Learning Abstract Synaptic pruning is the process of removing synapses in neural networks and has been considered to be a method of learning. During the developmental stages of the brain, synaptic pruning helps regulate efficiency and energy conservation. However, a destructive algorithm seems to be counter-intuitive to “le...

We describe an experimental study of pruning methods for decision tree classi ers in two learning situations: minimizing loss and probability estimation. In addition to the two most common methods for error minimization, CART's cost-complexity pruning and C4.5's errorbased pruning, we study the extension of cost-complexity pruning to loss and two pruning variants based on Laplace corrections. W...

One of the modern problems in field ecology is creation environmentally friendly equipment for monitoring and maintaining trees parks forests. The traditional use forest machines self-propelled tractors with internal combustion engines has a negative impact on environment as result pollutant emissions, i.e., products fuel residues. An alternative to this tradition can be mobile robots remote co...

The min-max modular neural network with Gaussian zerocrossing function (M-GZC) has locally tuned response characteristic and emergent incremental learning ability, but it suffers from quadratic complexity in storage space and response time. Redundant Sample pruning and redundant structure pruning can be considered to overcome these weaknesses. This paper aims at the latter; it analyzes the prop...

This paper presents a new class of pruning rule for unordered search. Previous pruning rules for unordered search identify operators that should not be applied in order to prune nodes reached via those operators. In contrast, the new pruning rules identify operators that should be applied and prune nodes that are not reached via those operators. Specific pruning rules employing both these appro...

This paper presents a new class of pruning axiom for unordered search. Previous pruning axioms for unordered search identify operators that should not be applied in order to prune states reached via those operators. In contrast, the new pruning axioms identify operators that should be applied and prune states that are not reached via those operators. Specific pruning axioms employing both these...

Recently, classifier ensemble methods are gaining more and more attention in the machine-learning and data-mining communities. In most cases, the performance of an ensemble is better than a single classifier. Many methods for creating diverse classifiers were developed during the past decade. When these diverse classifiers are generated, it is important to select the proper base classifier to j...

Neural network pruning methods on the level of individual network parameters (e.g. connection weights) can improve generalization, as is shown in this empirical study. However, an open problem in the pruning methods known today (e.g. OBD, OBS, autoprune, epsiprune) is the selection of the number of parameters to be removed in each pruning step (pruning strength). This work presents a pruning me...

When building classiication models, it is common practice to prune them to counter spurious eeects of the training data: this often improves performance and reduces model size. \Reduced-error pruning" is a fast pruning procedure for decision trees that is known to produce small and accurate trees. Apart from the data from which the tree is grown, it uses an independent \pruning" set, and prunin...

This paper presents a new algorithm, \Pressure Search," for growing min-max game trees. The algorithm is based on the idea of best-rst search. The goal of the search is to nd a strategy which will change the estimated value of the current position. The amount of pressure, deened as inversely proportional to the number of options available to the opponent, is used as a heuristic measure of the r...