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 -word2vec"

Positive matches

• 0.034 + - Recommender system
• 0.030 + - tfidf
• 0.030 + - Gradient descent
• 0.029 + - Text analysis
• 0.025 + - scikit-learn
• 0.024 + - Data clustering
• 0.024 + - Ulrike von Luxburg
• 0.024 + - Deep learning
• 0.023 + - Lexical feature selection
• 0.018 + - Yoav Freund
• 0.018 + - Ron Meir
• 0.018 + - Rob Schapire
• 0.017 + - How to study
• 0.017 + - Michael Ramscar
• 0.016 + - Pointwise mutual information
• 0.016 + - Privacy and Deanonymization
• 0.013 + - NLTK
• 0.012 + - Random forest
• 0.012 + - Jupyter/notebook
• 0.011 + - Poker

Negative matches

• 0.014 + - Graph embedding
• 0.010 + - Softmax function
• 0.008 + - Network geometry
• 0.003 + - Gender bias
• 0.003 + - Sentiment analysis
• 0.003 + - Chris McCormick
• 0.003 + - Word embedding
• 0.002 + - Softmax
• 0.002 + - GloVe
• 0.002 + - Continuous embedding
• 0.002 + - Paper/Hamilton2016
• 0.002 + - Paper/Perozzi2014
• 0.002 + - RNN
• 0.002 + - Clinical concept embedding
• 0.002 + - TensorFlow
• 0.002 + - Sentence embedding
• 0.002 + - Ilya Sutskever
• 0.001 + - Embedded topic model
• 0.001 + - Tomas Mikolov
• 0.001 + - sense2vec