cause) P(cause) Now, the Naïve Bayes model makes the following assumption: Although Effect1, …,Effectn might not be independent in general, they are independent given the value of Cause. (studyslide.com)
Z) Naïve Bayes Naïve Bayes uses assumption that the Xi are conditionally independent, given Y then: How many parameters need to be calculated? (slideum.com)
Classifier2
Introduction to Machine Learning Paulos Charonyktakis Maria Plakia Roadmap Supervised Learning Algorithms ◦ Artificial Neural Networks ◦ Naïve Bayes Classifier ◦ Decision Trees Application on VoIP in Wireless Networks Machine Learning The study of algorithms and systems that improve their performance with experience (Mitchell book) Experience? (slideum.com)
We develop and investigate several cross-lingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models. (aclanthology.org)
Datasets3
Statistical extraction of the alpha, beta, theta, delta and gamma brainwaves is performed to generate a large dataset that is then reduced to smaller datasets by feature selection using scores from OneR, Bayes Network, Information Gain, and Symmetrical Uncertainty. (researchgate.net)
We perform an extensive comparison of existing word and sentence representations on benchmark datasets addressing both graded and binary similarity.The best performing models outperform previous methods in both settings. (aclanthology.org)
Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. (aclanthology.org)
Parameters1
We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters. (aclanthology.org)
Classification2
Most word embeddings today are trained by optimizing a language modeling goal of scoring words in their context, modeled as a multi-class classification problem. (aclanthology.org)
Our framework is based on the well-studied problem of multi-label classification and, consequently, exposes several design choices for featurizing words and contexts, loss functions for training and score normalization. (aclanthology.org)
Network2
Original Problem may become separable (or easier) How to Train Multi-Layered Networks Select a network structure (number of hidden layers, hidden nodes, and connectivity). (slideum.com)
The explainability of the model can be considered at all stages of the development of artificial intelligence, both for initially interpreted AI models (linear and logistic regression, decision trees, and others), and for models based on the "black box" (perceptron, convolutional and recurrent neural networks, long-term short-term memory network, and others). (guidady.com)
Approaches1
Eleven XAI teams are exploring a wide range of methods and approaches for developing explainable models and effective explanation interfaces. (guidady.com)
Transfer1
We evaluate three alignment frameworks applied to these models: joint modeling, representation transfer learning, and sentence mapping, using parallel text to guide the alignment. (aclanthology.org)
Methods1
For models that are difficult to interpret by users, the most popular in the scientific community a posteriori methods of explanation (explainability after modeling) are LIME, SHAP and LRP. (guidady.com)
Machine3
The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. (w3we.com)
Many solutions that use AI algorithms are a kind of "black box" - often not only end users, but also the developers themselves cannot determine exactly how the machine learning model came to certain conclusions during the processing of the original data. (guidady.com)