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 -word2vec"

Positive matches

• 0.115 + - Class imbalance
• 0.031 + - tfidf
• 0.030 + - Neuron
• 0.030 + - Neural network
• 0.028 + - Text analysis
• 0.025 + - Recommender system
• 0.022 + - Data clustering
• 0.021 + - scikit-learn
• 0.020 + - Lexical feature selection
• 0.019 + - Pointwise mutual information
• 0.017 + - Ron Meir
• 0.016 + - Yoav Freund
• 0.015 + - Learning
• 0.013 + - Regular expression
• 0.013 + - Future of work
• 0.013 + - NLTK
• 0.012 + - Privacy and Deanonymization
• 0.010 + - Poker
• 0.010 + - Jupyter/notebook
• 0.010 + - IPython notebook

Negative matches

• 0.019 + - Graph embedding
• 0.008 + - Network geometry
• 0.004 + - Probability
• 0.004 + - Softmax
• 0.003 + - Softmax function
• 0.003 + - Paper/Hamilton2016
• 0.003 + - Word embedding
• 0.003 + - Sentiment analysis
• 0.003 + - Chris McCormick
• 0.002 + - Continuous embedding
• 0.002 + - Clinical concept embedding
• 0.002 + - Paper/Levy2014
• 0.002 + - TensorFlow
• 0.002 + - Gender bias
• 0.002 + - Tomas Mikolov
• 0.002 + - Sentence embedding
• 0.002 + - RNN
• 0.001 + - Paper/Perozzi2014
• 0.001 + - wiki2vec
• 0.001 + - Finkbeiner test