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  "Title": "Learned Pattern Similarity and Representation for Time Series",
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  "Description": "Learned Pattern Similarity (LPS) for time series, as\ndescribed in Baydogan and Runger (2016)\n<doi:10.1007/s10618-015-0425-y>. Implements an approach to\nmodel the dependency structure in time series that generalizes\nthe concept of autoregression to local auto-patterns. Generates\na pattern-based representation of time series along with a\nsimilarity measure called Learned Pattern Similarity (LPS).\nIntroduces a generalized autoregressive kernel. This package\nadapts C code from the 'randomForest' package by Andy Liaw and\nMatthew Wiener, itself based on original Fortran code by Leo\nBreiman and Adele Cutler.",
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      "page": "computeSimilarity",
      "title": "Compute similarity between time series based on learned patterns",
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        "computeSimilarity"
      ]
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      "page": "discriminativePatterns",
      "title": "Find and Visualize the Most Class-Discriminative Patterns",
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        "discriminativePatterns"
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    },
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      "title": "Extract a single tree from the ensemble.",
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    },
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      "title": "The Gun-Point Data",
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        "GunPoint"
      ]
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        "learnPattern",
        "learnPattern.default",
        "print.learnPattern"
      ]
    },
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      "page": "LPSNews",
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