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...

Gradient-boosted regression trees (GBRTs) have proven to be an effective solution to the learning-to-rank problem. This work proposes and evaluates techniques for training GBRTs that have efficient runtime characteristics. Our approach is based on the simple idea that compact, shallow, and balanced trees yield faster predictions: thus, it makes sense to incorporate some notion of execution cost...

The goal of statistical language modeling is to find probability estimates for arbitrary word sequences. To obtain non-zero values, the probability distributions found in the training data need to be smoothed. In the widely-used Kneser-Ney family of smoothing algorithms, this is achieved by absolute discounting. The discount parameters can be computed directly using some approximation formulas ...

A notorious problem in the application of neural networks is to nd a small suitable topology. High order perceptrons already solve a part of this problem as they require no hidden layers. However, the number of connections in a fully interlayer connected high order perceptron grows quickly with their order. Partially connected topologies are therefore highly desirable and can be obtained by app...

Neural network design aims for high classification accuracy and low network architecture complexity. It is also known that simultaneous optimization of both model accuracy and complexity improves generalization while avoiding overfitting on data. We describe a neural network training procedure that uses multi-objective optimization to evolve networks which are optimal both with respect to class...

Abs t rac t . Artificial neural networks can solve complex problems such as time Series prediction, handwritten pattern recognition or speech processing. Though software simulations are essential when one sets about to study a new algorithm, they cannot always fulfill real-time criteria required by some practical applications. Consequently, hardware implementations are of crucial import. The ap...

A novel method of introducing diversity into ensemble learning predictors for regression problems is presented. The proposed method prunes the ensemble while simultaneously training, as part of the same learning process. Here not all members of the ensemble are trained, but selectively trained, resulting in a diverse selection of ensemble members that have strengths in different parts of the tr...

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