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) +Learning +Gensim +Class imbalance +Lexical feature selection -word2vec"

Positive matches

• 0.043 + - Text analysis
• 0.037 + - Pointwise mutual information
• 0.028 + - tfidf
• 0.027 + - Recommender system
• 0.024 + - Gradient descent
• 0.024 + - scikit-learn
• 0.022 + - Data clustering
• 0.022 + - Ulrike von Luxburg
• 0.020 + - Data analysis
• 0.020 + - Deep learning
• 0.018 + - Natural language processing
• 0.017 + - Ron Meir
• 0.017 + - Yoav Freund
• 0.016 + - Rob Schapire
• 0.016 + - How to study
• 0.016 + - Michael Ramscar
• 0.014 + - NLTK
• 0.013 + - Regular expression
• 0.013 + - Future of work
• 0.012 + - Privacy and Deanonymization

Negative matches

• 0.021 + - Graph embedding
• 0.006 + - Network geometry
• 0.003 + - Softmax
• 0.003 + - Probability
• 0.003 + - Continuous embedding
• 0.002 + - Chris McCormick
• 0.002 + - Word embedding
• 0.002 + - Sentiment analysis
• 0.002 + - Tomas Mikolov
• 0.002 + - WordRank
• 0.002 + - Gender bias
• 0.002 + - Ilya Sutskever
• 0.002 + - Archetype
• 0.002 + - Neural network
• 0.002 + - Paper/Levy2014
• 0.002 + - Paper/Hamilton2016
• 0.001 + - Clinical concept embedding
• 0.001 + - Softmax function
• 0.001 + - Omer Levy
• 0.001 + - GloVe