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 +Gradient descent -word2vec"

Positive matches

• 0.136 + - Class imbalance
• 0.040 + - Recommender system
• 0.035 + - Neural network
• 0.033 + - Text analysis
• 0.033 + - tfidf
• 0.032 + - Neuron
• 0.031 + - scikit-learn
• 0.026 + - Data clustering
• 0.025 + - Ulrike von Luxburg
• 0.025 + - Lexical feature selection
• 0.020 + - Rob Schapire
• 0.020 + - Yoav Freund
• 0.019 + - Ron Meir
• 0.016 + - Learning
• 0.016 + - Pointwise mutual information
• 0.014 + - NLTK
• 0.014 + - Privacy and Deanonymization
• 0.013 + - IPython notebook
• 0.012 + - Poker
• 0.011 + - Natural language processing

Negative matches

• 0.025 + - Graph embedding
• 0.007 + - Network geometry
• 0.006 + - Softmax function
• 0.004 + - Sentence embedding
• 0.003 + - Gender bias
• 0.003 + - Word embedding
• 0.003 + - Sentiment analysis
• 0.003 + - TensorFlow
• 0.003 + - Clinical concept embedding
• 0.003 + - WordRank
• 0.002 + - Softmax
• 0.002 + - Paper/Levy2014a
• 0.002 + - Paper/Hamilton2016
• 0.002 + - Continuous embedding
• 0.002 + - Bias
• 0.002 + - Tomas Mikolov
• 0.002 + - wiki2vec
• 0.001 + - Chris McCormick
• 0.001 + - Paper/Perozzi2014
• 0.001 + - Dropout