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) -Rob Schapire +Yoav Freund -Gensim"

Positive matches

• 0.134 + - Class imbalance
• 0.038 + - Recommender system
• 0.034 + - Gradient descent
• 0.027 + - scikit-learn
• 0.026 + - Text analysis
• 0.026 + - Data clustering
• 0.025 + - Ulrike von Luxburg
• 0.020 + - Ron Meir
• 0.017 + - Learning
• 0.014 + - IPython notebook
• 0.014 + - NLTK
• 0.014 + - Privacy and Deanonymization
• 0.012 + - Poker
• 0.012 + - Jupyter/notebook
• 0.011 + - Pointwise mutual information
• 0.010 + - Future of work
• 0.010 + - Natural language processing
• 0.009 + - Regular expression
• 0.008 + - Regex
• 0.008 + - Random forest

Negative matches

• 0.034 + - word2vec
• 0.005 + - tfidf
• 0.003 + - Hierarchical softmax
• 0.002 + - Finkbeiner test
• 0.002 + - Paper/Hamilton2016
• 0.002 + - WordRank
• 0.002 + - Continuous embedding
• 0.002 + - Chris McCormick
• 0.001 + - Tomas Mikolov
• 0.001 + - Paper/Levy2014a
• 0.001 + - Graph embedding
• 0.001 + - Ilya Sutskever
• 0.001 + - Skip-gram
• 0.001 + - Jure Leskovec
• 0.001 + - Embedded topic model
• 0.001 + - Network geometry
• 0.001 + - Paper/Perozzi2014
• 0.001 + - Google
• 0.001 + - Dan Jurafsky
• 0.001 + - Omer Levy