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) +NLTK -Recommender system -Gensim"

Positive matches

• 0.116 + - Class imbalance
• 0.045 + - Natural language processing
• 0.034 + - Text analysis
• 0.032 + - Gradient descent
• 0.027 + - scikit-learn
• 0.026 + - Data clustering
• 0.026 + - Ulrike von Luxburg
• 0.020 + - Ron Meir
• 0.020 + - Rob Schapire
• 0.019 + - Yoav Freund
• 0.019 + - Lexical feature selection
• 0.015 + - tfidf
• 0.015 + - Learning
• 0.014 + - Pointwise mutual information
• 0.013 + - Jupyter/notebook
• 0.012 + - Poker
• 0.012 + - IPython notebook
• 0.012 + - Privacy and Deanonymization
• 0.008 + - Regex
• 0.008 + - Regular expression

Negative matches

• 0.034 + - Netflix prize
• 0.034 + - word2vec
• 0.003 + - Skip-gram
• 0.003 + - Hierarchical softmax
• 0.002 + - Finkbeiner test
• 0.002 + - WordRank
• 0.002 + - Continuous embedding
• 0.002 + - Probability
• 0.002 + - Chris McCormick
• 0.001 + - Tomas Mikolov
• 0.001 + - Paper/Levy2014a
• 0.001 + - wiki2vec
• 0.001 + - Graph embedding
• 0.001 + - Paper/Hamilton2016
• 0.001 + - Ilya Sutskever
• 0.001 + - Paper/Perozzi2014
• 0.001 + - Network geometry
• 0.001 + - Google
• 0.001 + - Dan Jurafsky
• 0.001 + - Omer Levy