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 +Ulrike von Luxburg -Gensim"

Positive matches

• 0.138 + - Class imbalance
• 0.035 + - Netflix prize
• 0.032 + - Gradient descent
• 0.025 + - scikit-learn
• 0.024 + - Data clustering
• 0.023 + - Text analysis
• 0.020 + - Learning
• 0.018 + - Yoav Freund
• 0.017 + - Pointwise mutual information
• 0.017 + - Privacy and Deanonymization
• 0.017 + - Rob Schapire
• 0.017 + - Future of work
• 0.013 + - NLTK
• 0.011 + - Regular expression
• 0.011 + - Poker
• 0.011 + - Jupyter/notebook
• 0.010 + - IPython notebook
• 0.008 + - Random forest
• 0.008 + - Artificial neural network
• 0.007 + - https://github.com/JohnLangford/vowpal_wabbit/wiki

Negative matches

• 0.032 + - word2vec
• 0.006 + - tfidf
• 0.004 + - Tomas Mikolov
• 0.004 + - Graph embedding
• 0.003 + - Hierarchical softmax
• 0.003 + - wiki2vec
• 0.002 + - Paper/Hamilton2016
• 0.001 + - sense2vec
• 0.001 + - Network geometry
• 0.001 + - WordRank
• 0.001 + - Paper/Levy2014a
• 0.001 + - Softmax function
• 0.001 + - Clinical concept embedding
• 0.001 + - Yoav Goldberg
• 0.001 + - William L. Hamilton
• 0.001 + - Paper
• 0.000 + - Finkbeiner test
• 0.000 + - Neural network
• 0.000 + - Paper/Levy2014
• 0.000 + - Paper/Perozzi2014