Articles About Machine Learning

Sub-sampling for Efficient Non-Parametric Bandit Exploration

In this paper we propose the first multi-armed bandit algorithm based on re-sampling that achieves asymptotically optimal regret simultaneously for different families of arms (namely Bernoulli, Gaussian and Poisson distributions). Unlike Thompson Sampling which requires to specify a different prior to be optimal in each case, our proposal RB-SDA does not need any distribution-dependent tuning… RB-SDA belongs to the family of Sub-sampling Duelling Algorithms (SDA) which combines the sub-sampling idea first used by the BESA [1] and SSMC [2] algorithms […]

October 28, 2020 Machine Learning

Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning

Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set up operational monitoring capabilities… Multi-temporal satellite images and publicly available webcam streams are among the viable data sources to monitor lake ice. In this work we investigate machine learning-based image […]

October 28, 2020 Machine Learning

Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning

Self-supervised visual pretraining has shown significant progress recently. Among those methods, SimCLR greatly advanced the state of the art in self-supervised and semi-supervised learning on ImageNet… The input feature representations for speech and visual tasks are both continuous, so it is natural to consider applying similar objective on speech representation learning. In this paper, we propose Speech SimCLR, a new self-supervised objective for speech representation learning. During training, Speech SimCLR applies augmentation on raw speech and its spectrogram. Its objective […]

October 28, 2020 Machine Learning

Error-Correcting Output Codes (ECOC) for Machine Learning

Machine learning algorithms, like logistic regression and support vector machines, are designed for two-class (binary) classification problems. As such, these algorithms must either be modified for multi-class (more than two) classification problems or not used at all. The Error-Correcting Output Codes method is a technique that allows a multi-class classification problem to be reframed as multiple binary classification problems, allowing the use of native binary classification models to be used directly. Unlike one-vs-rest and one-vs-one methods that offer a similar […]

October 26, 2020 Machine Learning

Why Use Ensemble Learning?

What are the Benefits of Ensemble Methods for Machine Learning? Ensembles are predictive models that combine predictions from two or more other models. Ensemble learning methods are popular and the go-to technique when the best performance on a predictive modeling project is the most important outcome. Nevertheless, they are not always the most appropriate technique to use and beginners the field of applied machine learning have the expectation that ensembles or a specific ensemble method are always the best method […]

October 24, 2020 Machine Learning

A Gentle Introduction to Ensemble Learning

Many decisions we make in life are based on the opinions of multiple other people. This includes choosing a book to read based on reviews, choosing a course of action based on the advice of multiple medical doctors, and determining guilt. Often, decision making by a group of individuals results in a better outcome than a decision made by any one member of the group. This is generally referred to as the wisdom of the crowd. We can achieve a […]

October 22, 2020 Machine Learning

6 Books on Ensemble Learning

Ensemble learning involves combining the predictions from multiple machine learning models. The effect can be both improved predictive performance and lower variance of the predictions made by the model. Ensemble methods are covered in most textbooks on machine learning; nevertheless, there are books dedicated to the topic. In this post, you will discover the top books on the topic of ensemble machine learning. After reading this post, you will know: Books on ensemble learning, including their table of contents and […]

October 19, 2020 Machine Learning

Softmax Activation Function with Python

Softmax is a mathematical function that converts a vector of numbers into a vector of probabilities, where the probabilities of each value are proportional to the relative scale of each value in the vector. The most common use of the softmax function in applied machine learning is in its use as an activation function in a neural network model. Specifically, the network is configured to output N values, one for each class in the classification task, and the softmax function […]

October 17, 2020 Algorithm, Data Science, Intermediate, Machine Learning, NLP, Python, Technique, Text, Topic Modeling, Unstructured Data, Unsupervised Leave a comment

Text Mining 101: A Stepwise Introduction to Topic Modeling using Latent Semantic Analysis (using Python)

Introduction Have you ever been inside a well-maintained library? I’m always incredibly impressed with the way the librarians keep everything organized, by name, content, and other topics. But if you gave these librarians thousands of books and asked them to arrange each book on the basis of their genre, they will struggle to accomplish this task in a day, let alone an hour! However, this won’t happen to you if these books came in a digital format, right? All the […]

October 17, 2020 Machine Learning

How to Develop LARS Regression Models in Python

Regression is a modeling task that involves predicting a numeric value given an input. Linear regression is the standard algorithm for regression that assumes a linear relationship between inputs and the target variable. An extension to linear regression involves adding penalties to the loss function during training that encourage simpler models that have smaller coefficient values. These extensions are referred to as regularized linear regression or penalized linear regression. Lasso Regression is a popular type of regularized linear regression that […]

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