Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
The Journal of Machine Learning Research
Compression-Based Averaging of Selective Naive Bayes Classifiers
The Journal of Machine Learning Research
Particle Swarm Model Selection
The Journal of Machine Learning Research
Particle Swarm Model Selection for Authorship Verification
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Model Selection: Beyond the Bayesian/Frequentist Divide
The Journal of Machine Learning Research
An energy-based model for region-labeling
Computer Vision and Image Understanding
Acute leukemia classification by ensemble particle swarm model selection
Artificial Intelligence in Medicine
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We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in the form of a table, with each example being encoded as a linear feature vector. Is it worth spending time incorporating domain knowledge in feature construction or algorithm design, or can off-the-shelf programs working directly on simple low-level features do better than skilled data analysts? To answer these questions, we formatted five datasets using two data representations. The participants in the ''prior knowledge'' track used the raw data, with full knowledge of the meaning of the data representation. Conversely, the participants in the ''agnostic learning'' track used a pre-formatted data table, with no knowledge of the identity of the features. The results indicate that black-box methods using relatively unsophisticated features work quite well and rapidly approach the best attainable performance. The winners on the prior knowledge track used feature extraction strategies yielding a large number of low-level features. Incorporating prior knowledge in the form of generic coding/smoothing methods to exploit regularities in data is beneficial, but incorporating actual domain knowledge in feature construction is very time consuming and seldom leads to significant improvements. The AL vs. PK challenge web site remains open for post-challenge submissions: http://www.agnostic.inf.ethz.ch/.