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 -Gensim"

Positive matches

• 0.040 + - Gradient descent
• 0.035 + - Netflix prize
• 0.025 + - Data clustering
• 0.024 + - Ulrike von Luxburg
• 0.022 + - scikit-learn
• 0.021 + - Text analysis
• 0.018 + - Rob Schapire
• 0.018 + - Ron Meir
• 0.018 + - Learning
• 0.018 + - Pointwise mutual information
• 0.016 + - Regular expression
• 0.013 + - NLTK
• 0.013 + - Future of work
• 0.012 + - IPython notebook
• 0.011 + - Poker
• 0.010 + - Privacy and Deanonymization
• 0.010 + - Jupyter/notebook
• 0.010 + - Regex
• 0.009 + - Neural network
• 0.007 + - Python#Modules_.26_Libraries

Negative matches

• 0.032 + - word2vec
• 0.004 + - tfidf
• 0.003 + - Network geometry
• 0.002 + - Tomas Mikolov
• 0.002 + - Graph embedding
• 0.002 + - Hierarchical softmax
• 0.002 + - Sentence embedding
• 0.002 + - Paper/Levy2014a
• 0.002 + - sense2vec
• 0.001 + - Paper/Hamilton2016
• 0.001 + - Paper/Levy2014
• 0.001 + - Ilya Sutskever
• 0.001 + - Closure tools
• 0.001 + - t-SNE
• 0.001 + - TensorFlow
• 0.001 + - Clinical concept embedding
• 0.001 + - Recurrent neural network
• 0.001 + - Skip-gram
• 0.001 + - Softmax function
• 0.001 + - Cecilia Mascolo