Abstract. Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base ” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative

Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little

Many real-world machine learning tasks are faced with the problem of small training sets. Additionally, the class distribution of the training set often does not match the target distribution. In this paper we compare the performance of many learning models on a substantial benchmark of binary

Many real-world machine learning tasks are faced with the problem of small training sets. Additionally, the class distribution of the training set often does not match the target distribution. In this paper we compare the performance of many learning models on a substantial benchmark of binary

that changing the balance of negative and positive training examples has little effect on the classifiers produced by standard Bayesian and decision tree learning methods. Accordingly, the recommended way of applying one of these methods in a domain with differing misclassification costs is to learn a

for training, even if these skills are general. The model can lead to multiple equilibria. In one equilibrium quits are endogenously high, and as a result employers have limited monopsony power and provide little training, while in another equilibrium quits are low and training is high. Using microdata

on performance, demonstrating that as little as 100,000 words of training data is adequate to get performance around 90% on newswire. Although we present our understanding of why this algorithm performs so well on this class of problems, we believe that significant improvement in performance may still

Natural language understanding is an essential module in any dialogue system. To obtain satisfactory performance levels, a dialogue system needs a semantic parser/natural language understanding

, I thought there must be a shortcut. A couple of us Piagetians gathered and devel-oped a little web site for developmentalists interested in Rasch analysis. When we tried to sug-gest introductory readings for our colleagues — many of whom had very little explicit training in statistics

for simultaneous spike trains are also discussed. For two-train comparisons of irregularly discharging nerve cells, moderate nonstationarities areshown tohave little effecton the detection of interactions. Combining repetitive stimulation and simultaneous recording of spike trains from two (or more) neurons yields