Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem -Probability, Paradox, and the Reasonable Person Principle +Cross validation +Cluster analysis +Machine learning +Machine learning for healthcare +tf-idf +Boosting (machine learning) +scikit-learn +PyTorch +Ulrike von Luxburg -Gensim"

Positive matches

• 0.125 + - Class imbalance
• 0.046 + - skorch
• 0.041 + - Decision tree
• 0.033 + - PyTorch-BigGraph
• 0.032 + - Recommender system
• 0.031 + - Gradient descent
• 0.024 + - Data clustering
• 0.023 + - Text analysis
• 0.019 + - Random forest
• 0.018 + - Ron Meir
• 0.018 + - Yoav Freund
• 0.018 + - Rob Schapire
• 0.017 + - tfidf
• 0.017 + - Lexical feature selection
• 0.016 + - Pointwise mutual information
• 0.015 + - Python/Modules
• 0.015 + - Privacy and Deanonymization
• 0.015 + - Learning
• 0.013 + - NLTK
• 0.013 + - IPython notebook

Negative matches

• 0.030 + - word2vec
• 0.003 + - Graph embedding
• 0.003 + - wiki2vec
• 0.002 + - Hierarchical softmax
• 0.002 + - Paper/Hamilton2016
• 0.001 + - sense2vec
• 0.001 + - Skip-gram
• 0.001 + - Ilya Sutskever
• 0.001 + - Network geometry
• 0.001 + - Softmax function
• 0.001 + - Clinical concept embedding
• 0.001 + - WordRank
• 0.001 + - Paper/Levy2014
• 0.001 + - Paper/Levy2014a
• 0.001 + - William L. Hamilton
• 0.001 + - Google
• 0.001 + - Tomas Mikolov
• 0.001 + - TensorFlow
• 0.001 + - Residual Learning
• 0.000 + - Finkbeiner test