Boosting as a Product of Experts

In this paper, we derive a novel probabilistic model of boosting as a Product of Experts. We re-derive the boosting algorithm as a greedy incremental model selection procedure which ensures that addition of new experts to the ensemble does not decrease the likelihood of the data. These learning rules lead to a generic boosting algorithm - POE- Boost which turns out to be similar to the AdaBoost algorithm under certain assumptions on the expert probabilities. The paper then extends the POEBoost algorithm to POEBoost.CS which handles hypothesis that produce probabilistic predictions. This new algorithm is shown to have better generalization performance compared to other state of the art algorithms

Boosted Generative Models

We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our meta-algorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent deep expressive models. Further, our approach allows the ensemble to include discriminative models trained to distinguish real data from model-generated data. We show theoretical conditions under which incorporating a new model in the ensemble will improve the fit and empirically demonstrate the effectiveness of our black-box boosting algorithms on density estimation, classification, and sample generation on benchmark datasets for a wide range of generative models.Comment: AAAI 201

Phoneme and sentence-level ensembles for speech recognition

We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this, we compare two different boosting schemes, one at the phoneme level and one at the utterance level, with a phoneme-level bagging scheme. We control for many parameters and other choices, such as the state inference scheme used. In an unbiased experiment, we clearly show that the gain of boosting methods compared to a single hidden Markov model is in all cases only marginal, while bagging significantly outperforms all other methods. We thus conclude that bagging methods, which have so far been overlooked in favour of boosting, should be examined more closely as a potentially useful ensemble learning technique for speech recognition

Pengembangan Media Pembelajaran Berbasis Aplikasi Lectora Inspire untuk Meningkatkan Pemahaman Matematis dalam Penerapan Problem Based Learning

Researchers created a learning medium based on the Lectora Inspire Application based on the findings of a requirements analysis throughout the learning process. The goal of this study is to make this product more legitimate, practical, and useful in boosting students' mathematical comprehension. The ADDIE (Analyze, create, Development, Implementation, and Evaluation) development paradigm was used by researchers to create this study and development. This study included 30 students from class XI Science 1 SMA Negeri 1 Simanindo. According to the research findings, the produced Lectora Inspire Application-based learning media has a high degree of validity, with an average percentage of 87.08% according to material experts and 89.61 according to media experts. Positive reactions from instructors and students were also discovered during small group and large group trials, with an average score of 92% from teachers, 94.6% from small groups, and 90.2% from big groups. This demonstrates that this medium is quite useful in the learning process. Aside from that, the outcomes of applying this interactive learning media demonstrate its efficacy. The completion rate of classical learning was 86.67%, and the efficacy questionnaire received an average percentage of 93.44%. The development in pupils' mathematical comprehension might be classified as modest. Based on the findings of this study, it is possible to conclude that the Lectora Inspire application-based learning media produced satisfied the requirements of being valid, practical, and successful in boosting students' mathematical comprehension