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) +Recommender system +Ron Meir -Gensim"

Positive matches

• 0.152 + - Class imbalance
• 0.037 + - Netflix prize
• 0.035 + - Gradient descent
• 0.029 + - scikit-learn
• 0.026 + - Text analysis
• 0.026 + - Data clustering
• 0.024 + - Ulrike von Luxburg
• 0.020 + - Yoav Freund
• 0.020 + - Rob Schapire
• 0.018 + - Learning
• 0.016 + - Future of work
• 0.016 + - Privacy and Deanonymization
• 0.014 + - NLTK
• 0.013 + - Pointwise mutual information
• 0.013 + - IPython notebook
• 0.012 + - Poker
• 0.011 + - Jupyter/notebook
• 0.011 + - Regular expression
• 0.010 + - Regex
• 0.009 + - Natural language processing

Negative matches

• 0.034 + - word2vec
• 0.006 + - tfidf
• 0.002 + - Hierarchical softmax
• 0.002 + - Graph embedding
• 0.002 + - Chris McCormick
• 0.002 + - Finkbeiner test
• 0.002 + - wiki2vec
• 0.001 + - Paper/Hamilton2016
• 0.001 + - Ilya Sutskever
• 0.001 + - GloVe
• 0.001 + - Paper/Levy2014a
• 0.001 + - Tomas Mikolov
• 0.001 + - Paper/Levy2014
• 0.001 + - Continuous embedding
• 0.001 + - Skip-gram
• 0.001 + - sense2vec
• 0.001 + - Yoav Goldberg
• 0.001 + - Google
• 0.001 + - Jure Leskovec
• 0.001 + - Embedded topic model