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) +Gensim +Ulrike von Luxburg +Gradient descent +Rob Schapire +Text analysis -word2vec"

Positive matches

• 0.117 + - Class imbalance
• 0.051 + - Lexical feature selection
• 0.032 + - Data analysis
• 0.032 + - Neural network
• 0.028 + - Recommender system
• 0.025 + - tfidf
• 0.022 + - Neuron
• 0.021 + - Data clustering
• 0.021 + - scikit-learn
• 0.020 + - Learning
• 0.019 + - Science
• 0.018 + - Pointwise mutual information
• 0.016 + - Yoav Freund
• 0.016 + - Ron Meir
• 0.015 + - Regular expression
• 0.013 + - NLTK
• 0.010 + - Poker
• 0.010 + - IPython notebook
• 0.010 + - Privacy and Deanonymization
• 0.010 + - Interdisciplinary research

Negative matches

• 0.014 + - Graph embedding
• 0.007 + - Network geometry
• 0.007 + - Softmax function
• 0.003 + - GloVe
• 0.003 + - Paper/Perozzi2014
• 0.003 + - Continuous embedding
• 0.003 + - Chris McCormick
• 0.002 + - Skip-gram
• 0.002 + - Softmax
• 0.002 + - Word embedding
• 0.002 + - Paper/Levy2014
• 0.002 + - Clinical concept embedding
• 0.002 + - Gender bias
• 0.002 + - Sentiment analysis
• 0.002 + - Archetype
• 0.001 + - TensorFlow
• 0.001 + - Ilya Sutskever
• 0.001 + - Paper/Levy2014a
• 0.001 + - Orthogonal Procrustes problem
• 0.001 + - Machine translation