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 -Yoav Freund -Class imbalance +NLTK -Gensim"

Positive matches

• 0.034 + - Natural language processing
• 0.033 + - Netflix prize
• 0.028 + - Gradient descent
• 0.023 + - Text analysis
• 0.022 + - Data clustering
• 0.022 + - Ulrike von Luxburg
• 0.021 + - scikit-learn
• 0.020 + - Lexical feature selection
• 0.020 + - Learning
• 0.019 + - Pointwise mutual information
• 0.017 + - Ron Meir
• 0.016 + - Rob Schapire
• 0.016 + - Future of work
• 0.013 + - Privacy and Deanonymization
• 0.013 + - Regular expression
• 0.010 + - Poker
• 0.010 + - IPython notebook
• 0.010 + - Jupyter/notebook
• 0.008 + - Regex
• 0.008 + - tfidf

Negative matches

• 0.029 + - word2vec
• 0.005 + - sense2vec
• 0.005 + - Graph embedding
• 0.004 + - Probability
• 0.002 + - Hierarchical softmax
• 0.002 + - wiki2vec
• 0.002 + - Softmax function
• 0.002 + - Continuous embedding
• 0.001 + - Network geometry
• 0.001 + - Paper/Levy2014
• 0.001 + - Gender bias
• 0.001 + - Atwood's law
• 0.001 + - GloVe
• 0.001 + - Generative adversarial network
• 0.000 + - Embedded topic model
• 0.000 + - Paper
• 0.000 + - Sentence embedding
• 0.000 + - Clinical concept embedding
• 0.000 + - Chris McCormick
• 0.000 + - Rami Al-Rfou