Tag Archives: Model Selection

LM101-077: How to Choose the Best Model using BIC

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Episode Summary: In this episode, we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike Information Criterion) model selection methods. Briefly, BIC is used to estimate the probability of the training data given the probability model, while AIC is used to estimate out-of-sample prediction… Read More »

LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications

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LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications Episode Summary: In this episode we discuss Latent Semantic Indexing type machine learning algorithms which have a probabilistic interpretation. We explain why such a probabilistic interpretation is important and discuss how such algorithms can be used in the design of document retrieval… Read More »

LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)

What robot is thinking when it classifies a dog image as the word dog.

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LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging) Episode Summary: Deep learning machine technology has rapidly developed over the past five years due in part to a variety of factors such as: better technology, convolutional net algorithms, rectified linear units, and a relatively new learning strategy called “dropout” in which hidden… Read More »