Titlebook: Machine Learning and Knowledge Discovery in Databases; International Worksh Peggy Cellier,Kurt Driessens Conference proceedings 2020 Spring

多骨

Automating Common Data Science Matrix Transformationsf the right primitives (using the appropriate libraries) to get the most elegant code transformation is not always easy. In this paper, we present the first system that is able to automatically synthesise program snippets in R given an input data matrix and an output matrix, partially filled by the

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DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasetsare still in the hands of well-educated and -funded experts only. To help to democratize machine learning, we propose DeepNotebooks as a novel way to empower a broad spectrum of users, which are not machine learning experts, but might have some basic programming skills and are interested data scienc

ARCH

隼鷹

Meta-learning of Textual Representationsarning problem. Whereas these methods are quite effective, they are still limited in the sense that they work for tabular (matrix formatted) data only. This paper describes one step forward in trying to automate the design of supervised learning methods in the context of text mining. We introduce a

dysphagia

ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optim training. Each operation has a set of hyper-parameters, which can become irrelevant for the pipeline when the operation is not selected. This gives rise to a hierarchical conditional hyper-parameter space. To optimize this mixed continuous and discrete conditional hierarchical hyper-parameter space

白楊魚

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SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract)dels integrate data with predictive and descriptive models, in a way that is reminiscent of inductive databases. SynthLog provides primitives for learning and manipulating inductive data models, it supports data wrangling, learning predictive models and constraints, and probabilistic and constraint

Countermand

The , Python Library for Automated Feature Engineering and Selectionies. Complex non-linear machine learning models such as neural networks are in practice often difficult to train and even harder to explain to non-statisticians, who require transparent analysis results as a basis for important business decisions. While linear models are efficient and intuitive, the

guardianship

誘導(dǎo)

Towards Automated Configuration of Stream Clustering Algorithmstering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the interne