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ALTERNATIVE LEARNING METHODS BACKTEST
Dataset: 277 rows | Rep power: p1.07 | Hit threshold: 18.0px
Baselines: linear MAE≈12.24 | WiSARD K=12 MAE≈9.93
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Method                                 MAE     Hit%  F50 MAE  L50 MAE   vs Lin
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Linear (baseline)                    10.18    80.1%    20.74     8.19      ---
WiSARD K=12 (reference)               9.93     ~55%      ---      ---    -2.31
1. Echo State Net (res=512)           8.29    85.2%    15.76     2.11    -1.89
2. Kanerva SDM (addr=2000)           10.16    80.1%    20.74     8.19    -0.02
3. N-gram Markov (4×69 chunks)       10.11    80.5%    20.36     8.19    -0.07
4. Bloom Filter (8192 slots)         17.08    59.2%    23.67    13.15    +6.90
5. HDC (n_hd=2000, 32 cls)           65.33     0.0%    56.11    65.33   +55.15
6. RandSubspace (30×50bits)           8.31    90.6%    15.45     6.00    -1.88
7. WiSARD+Elig (k=12,tr=5)            8.24    90.3%    15.21     5.74    -1.94

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RANKING by MAE (lower is better, rep power p1.07)
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Rank  Method                                 MAE     Hit%      F50      L50
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1     7. WiSARD+Elig (k=12,tr=5)            8.24    90.3%    15.21     5.74
2     1. Echo State Net (res=512)           8.29    85.2%    15.76     2.11
3     6. RandSubspace (30×50bits)           8.31    90.6%    15.45     6.00
4     3. N-gram Markov (4×69 chunks)       10.11    80.5%    20.36     8.19
5     2. Kanerva SDM (addr=2000)           10.16    80.1%    20.74     8.19
6     4. Bloom Filter (8192 slots)         17.08    59.2%    23.67    13.15
7     5. HDC (n_hd=2000, 32 cls)           65.33     0.0%    56.11    65.33

  Linear baseline:   MAE=10.18  Hit=80.1%
  WiSARD K=12 ref:   MAE=9.93   Hit=~55%

Notes:
  F50/L50 = MAE on first/last 50 samples (learning speed proxy).
  Hit% = fraction within 18px (Robocode bullet half-width).
  All methods: online, binary input, no gradients, no supervised labels.
  Learning signal: residual correction after linear extrapolation at p1.07.