Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes local smoothness constraints among data into account during ensemble learning. In this paper, we introduce a local smo...

In machine learning ensemble methods have demonstrated high accuracy for the variety of problems in different areas. The most known algorithms intensively used in practice are random forests and gradient boosting. In this paper we present InfiniteBoost — a novel algorithm, which combines the best properties of these two approaches. The algorithm constructs the ensemble of trees for which two pr...

We present an approach and a system that explores the application of interactive machine learning to a branching program-based boosting algorithm—Martingale Boosting. Typically, its performance is based on the ability of a learner to meet a fixed objective and does not account for preferences (e.g., low FPs) arising from an underlying classification problem. We use user preferences gathered on ...

Oza’s Online Boosting algorithm provides a version of AdaBoost which can be trained in an online way for stationary problems. One perspective is that this enables the power of the boosting framework to be applied to datasets which are too large to fit into memory. The online boosting algorithm assumes the data distribution to be independent and identically distributed (i.i.d.) and therefore has...

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability i...

Boosting is an iterative ensemble-learning paradigm. Every iteration, a weak predictor learns a classification task, taking into account performance achieved in previous iterations. This is done by assigning weights to individual records of the dataset, which are increased if the record is misclassified by the previous weak predictor. Hence, subsequent predictors learn to focus on problematic r...

In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We present an adaptation of several popular ensemble techniques in machine learnin...

In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We present an adaptation of several popular ensemble techniques in machine learnin...

Machine Learning (ML) has been successfully applied to a wide range of domains and applications. One of the techniques behind most of these successful applications is Ensemble Learning (EL), the field of ML that gave birth to methods such as Random Forests or Boosting. The complexity of applying these techniques together with the market scarcity on ML experts, has created the need for systems t...

Semi-supervised learning has been recognized as an effective way to improve acoustic model training in cases where sufficient transcribed data are not available. Different from most of existing approaches only using single acoustic model and focusing on how to refine it, this paper investigates the feasibility of using ensemble methods for semi-supervised acoustic modeling training. Two methods...