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Found 11 papers, 1 papers with code
Probit Normal Correlated Topic Models
Our use of the probit model in the context of topic discovery is novel, as many authors have so far con- centrated solely of the logistic model partly due to the formidable inefficiency of the multinomial probit model even in the case of very small topical spaces.
A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining
In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness.
Adaptive Random SubSpace Learning (RSSL) Algorithm for Prediction
Sixth, use the model for prediction purpose on test set of the data.
On the Predictive Properties of Binary Link Functions
This paper provides a theoretical and computational justification of the long held claim that of the similarity of the probit and logit link functions often used in binary classification.
Random Subspace Learning Approach to High-Dimensional Outliers Detection
We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning.
On the Statistical Challenges of Echo State Networks and Some Potential Remedies
We create, develop and implement a family of predictably optimal robust and stable ensemble of Echo State Networks via regularizing the training and perturbing the input.
Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training
Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years.
Naive Dictionary On Musical Corpora: From Knowledge Representation To Pattern Recognition
Given the novelty and therefore the extremely difficulty of properly forming a complete version of a dictionary of muselets, the present paper focuses on a simpler albeit naive version of the ultimate dictionary, which we refer to as a Naive Dictionary because of the fact that all the words are of the same length.
A Text Mining Discovery of Similarities and Dissimilarities Among Sacred Scriptures
The careful examination of sacred texts gives valuable insights into human psychology, different ideas regarding the organization of societies as well as into terms like truth and God.
Other Statistics
A Computational Exploration of Emerging Methods of Variable Importance Estimation
PERF and XGBOOST are "data-hungry" methods, they had the worst performance on small data sizes but they are the fastest when it comes to the execution time.
Improving the Predictive Performances of $k$ Nearest Neighbors Learning by Efficient Variable Selection
This paper computationally demonstrates a sharp improvement in predictive performance for $k$ nearest neighbors thanks to an efficient forward selection of the predictor variables.
