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 -Data clustering +Gradient descent -EM algorithm +scikit-learn +tf-idf +NLTK -Gensim"

Positive matches

• 0.109 + - Class imbalance
• 0.034 + - Natural language processing
• 0.033 + - Neural network
• 0.029 + - Neuron
• 0.025 + - Text analysis
• 0.025 + - tfidf
• 0.024 + - Recommender system
• 0.022 + - Ulrike von Luxburg
• 0.022 + - Lexical feature selection
• 0.021 + - Pointwise mutual information
• 0.021 + - Decision tree
• 0.016 + - Random forest
• 0.015 + - PyTorch
• 0.013 + - Python/Modules
• 0.012 + - Learning
• 0.012 + - IPython notebook
• 0.011 + - Future of work
• 0.011 + - Privacy and Deanonymization
• 0.011 + - skorch
• 0.010 + - Regular expression

Negative matches

• 0.028 + - word2vec
• 0.005 + - sense2vec
• 0.005 + - Graph embedding
• 0.003 + - wiki2vec
• 0.003 + - Probability
• 0.002 + - Hierarchical softmax
• 0.001 + - Clinical concept embedding
• 0.001 + - Paper/Hamilton2016
• 0.001 + - Paper/Levy2014
• 0.001 + - Softmax function
• 0.001 + - Network geometry
• 0.001 + - Paper/Perozzi2014
• 0.001 + - Omer Levy
• 0.001 + - Gender bias
• 0.001 + - Jure Leskovec
• 0.001 + - Skip-gram
• 0.001 + - Yoav Goldberg
• 0.000 + - GloVe
• 0.000 + - Chris McCormick
• 0.000 + - Embedded topic model